R Notebook
Single cell seq after sorting for PhenoID
sample1 = Neurons1 sample2 = Neurons2 sample3 = Glia1 - Astrocytes (CD44+) sample4 = Glia2 - Radial Glia (CD44-)
In HPC I have run steps of scrnabox (custom pipeline in progress) 1. Cell Ranger for feature seq 2. Create Seurat Objects 3. Apply minimum filtering and calculate percent mitochondria.
I have technical 3 replicates with hashtag labels at this point I haven’t yet demultiplex the hashtags. The data here will be treated as one sample. I sorted three separate samples and pooled them together.
# set up the environment
library(Seurat)Attaching SeuratObject
Attaching splibrary(dplyr)
Attaching package: ‘dplyr’
The following objects are masked from ‘package:stats’:
filter, lag
The following objects are masked from ‘package:base’:
intersect, setdiff, setequal, unionlibrary(Matrix)
library(ggplot2)
#rm(list = ls())
Read in the seurat objects made in compute canada
Glia2
An object of class Seurat
33541 features across 10338 samples within 2 assays
Active assay: RNA (33538 features, 0 variable features)
1 other assay present: HTOHave a look at the objects that already have some filtering
See the violin plots
VlnPlot(Neurons1, pt.size = 0.10, features = c("nFeature_RNA", "nCount_RNA", "percent.mt"), ncol = 3)
VlnPlot(Neurons1, pt.size = 0.10, features = c("nFeature_RNA"), y.max = 500)Warning: Removed 873 rows containing non-finite values (stat_ydensity).
Warning: Removed 873 rows containing missing values (geom_point).
VlnPlot(Neurons1, pt.size = 0.10, features = c("nCount_RNA"), y.max = 2000)Warning: Removed 546 rows containing non-finite values (stat_ydensity).
Warning: Removed 546 rows containing missing values (geom_point).
# filter more cells
Neuron1.ft <- subset(Neurons1, subset = nFeature_RNA > 250 & nCount_RNA > 250 & nCount_RNA < 10000)
Neuron1.ftAn object of class Seurat
33541 features across 1833 samples within 2 assays
Active assay: RNA (33538 features, 0 variable features)
1 other assay present: HTO# 33541 features across 1833 samples
Neurons 2 - CD56++
VlnPlot(Neurons2, pt.size = 0.10, features = c("nFeature_RNA", "nCount_RNA", "percent.mt"), ncol = 3)
VlnPlot(Neurons2, pt.size = 0.10, features = c("nFeature_RNA"), y.max = 500)Warning: Removed 5653 rows containing non-finite values (stat_ydensity).
Warning: Removed 5653 rows containing missing values (geom_point).
VlnPlot(Neurons2, pt.size = 0.10, features = c("nFeature_RNA"), y.max = 1000)Warning: Removed 2379 rows containing non-finite values (stat_ydensity).
Warning: Removed 2379 rows containing missing values (geom_point).
VlnPlot(Neurons2, pt.size = 0.10, features = c("nCount_RNA"), y.max = 2000)Warning: Removed 2264 rows containing non-finite values (stat_ydensity).
Warning: Removed 2264 rows containing missing values (geom_point).
# filter more cells
Neuron2.ft <- subset(Neurons2, subset = nFeature_RNA > 500 & nCount_RNA > 500 & nCount_RNA < 10000)
Neuron2.ftAn object of class Seurat
33541 features across 5190 samples within 2 assays
Active assay: RNA (33538 features, 0 variable features)
1 other assay present: HTOGlia1 - Astrocyte data
VlnPlot(Glia1, pt.size = 0.10, features = c("nFeature_RNA", "nCount_RNA", "percent.mt"), ncol = 3)
VlnPlot(Glia1, pt.size = 0.10, features = c("nFeature_RNA"), y.max = 5000)Warning: Removed 82 rows containing non-finite values (stat_ydensity).
Warning: Removed 82 rows containing missing values (geom_point).
VlnPlot(Glia1, pt.size = 0.10, features = c("nFeature_RNA"), y.max = 1000)Warning: Removed 11811 rows containing non-finite values (stat_ydensity).
Warning: Removed 11811 rows containing missing values (geom_point).
VlnPlot(Glia1, pt.size = 0.10, features = c("nCount_RNA"), y.max = 1000)Warning: Removed 25751 rows containing non-finite values (stat_ydensity).
Warning: Removed 25751 rows containing missing values (geom_point).
VlnPlot(Glia1, pt.size = 0.10, features = c("nCount_RNA"), y.max = 12000)Warning: Removed 252 rows containing non-finite values (stat_ydensity).
Warning: Removed 252 rows containing missing values (geom_point).
# extreme filter
Glia1.ft <- subset(Glia1, subset = nFeature_RNA > 500 & nCount_RNA > 300 & nCount_RNA < 10000)
Glia1.ftAn object of class Seurat
33541 features across 37813 samples within 2 assays
Active assay: RNA (33538 features, 0 variable features)
1 other assay present: HTOGlia1An object of class Seurat
33541 features across 54723 samples within 2 assays
Active assay: RNA (33538 features, 0 variable features)
1 other assay present: HTOVlnPlot(Glia1.ft, pt.size = 0.10, features = c("nFeature_RNA", "nCount_RNA", "percent.mt"), ncol = 3)
NA
NA
NA
NA
NAGlia2 - Radial Glia
## Filter Glia 2
VlnPlot(Glia2, pt.size = 0.10, features = c("nFeature_RNA", "nCount_RNA", "percent.mt"), ncol = 3)
VlnPlot(Glia2, pt.size = 0.10, features = c("nFeature_RNA"), y.max = 5000)Warning: Removed 61 rows containing non-finite values (stat_ydensity).
Warning: Removed 61 rows containing missing values (geom_point).
VlnPlot(Glia2, pt.size = 0.10, features = c("nFeature_RNA"), y.max = 1000)Warning: Removed 2435 rows containing non-finite values (stat_ydensity).
Warning: Removed 2435 rows containing missing values (geom_point).
VlnPlot(Glia2, pt.size = 0.10, features = c("nCount_RNA"), y.max = 1000)Warning: Removed 3194 rows containing non-finite values (stat_ydensity).
Warning: Removed 3194 rows containing missing values (geom_point).
VlnPlot(Glia2, pt.size = 0.10, features = c("nCount_RNA"), y.max = 12000)Warning: Removed 199 rows containing non-finite values (stat_ydensity).
Warning: Removed 199 rows containing missing values (geom_point).
# extreme filter
Glia2.ft <- subset(Glia1, subset = nFeature_RNA > 500 & nCount_RNA > 500 & nCount_RNA < 10000)
Glia2.ftAn object of class Seurat
33541 features across 37813 samples within 2 assays
Active assay: RNA (33538 features, 0 variable features)
1 other assay present: HTOVlnPlot(Glia1.ft, pt.size = 0.10, features = c("nFeature_RNA", "nCount_RNA", "percent.mt"), ncol = 3)
# there are so many suposed cells I am concerned the high read cells are actually doublets.
Analyze each dataset - get clusters
# cluster the neurons
seu <- Neuron1.ft
seu$orig.ident <- 'Neurons1'
seu <- NormalizeData(seu, normalization.method = "LogNormalize", scale.factor = 10000)Performing log-normalization
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|seu <- FindVariableFeatures(seu, selection.method = "vst", nfeatures = 2000)Calculating gene variances
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating feature variances of standardized and clipped values
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|# Identify the 10 most highly variable genes
top10 <- head(VariableFeatures(seu), 10)
# plot variable features with and without labels
plot1 <- VariableFeaturePlot(seu)
plot2 <- LabelPoints(plot = plot1, points = top10, repel = TRUE)When using repel, set xnudge and ynudge to 0 for optimal resultsseu <- ScaleData(seu)Centering and scaling data matrix
|
| | 0%
|
|================================================ | 50%
|
|================================================================================================| 100%seu <- RunPCA(seu)PC_ 1
Positive: CDH19, MPZ, COL4A1, COL4A2, COL3A1, ZEB2, CTSC, OLFML2A, MIA, FN1
NRXN1, ERBB3, TGFBR2, COL14A1, COL1A2, FST, COL5A2, COL28A1, PLAT, SHC4
NTM, PMEPA1, KCTD12, CAVIN3, SOX10, LAMC1, BGN, IFI16, LIMCH1, GAS2L3
Negative: CLU, PTGDS, DLK1, SPARCL1, PTN, APOE, SAT1, TFPI2, GDF10, HPD
TMSB4X, C1orf61, MGST1, TRH, NRIP3, RBP4, WIF1, NUPR1, IGFBP7, LIX1
FGFBP1, ESM1, TPPP3, GNG11, BAALC-AS2, BAALC, LY6H, WNT2B, SFRP2, CRYAB
PC_ 2
Positive: CELF4, ANK3, CACNA2D1, PCDH9, NCKAP5, CHGB, SYT1, NEUROD1, PCLO, GNB3
OTX2, STMN2, PTPRR, INA, OCIAD2, IMPG2, DCX, DYNC1I1, SSTR2, ZFHX4
BTBD8, STMN1, GRIA2, MARCH1, SLC1A2, ATP1A3, STMN4, AMER2, BEX1, FAM19A4
Negative: CDH19, COL3A1, MPZ, CTSC, OLFML2A, COL4A2, COL4A1, MIA, VIM, FN1
TGFBR2, COL1A2, ERBB3, ZEB2, S100B, SPARC, COL14A1, S100A10, IFITM3, COL5A2
CAVIN3, PLAT, COL28A1, SOX10, FST, PLEKHA4, GAS2L3, CXCL12, ITIH5, LGALS1
PC_ 3
Positive: PCAT4, IMPG2, NEUROD1, NRXN1, CDH19, PLPPR4, TPH1, SYT1, ZEB2, MPZ
MIA, GSG1, STMN2, OLFML2A, SST, OLFM3, FAM19A4, CTSC, GNB3, PTPRR
BTBD8, COL3A1, GAS2L3, BCAT1, SOX10, COL4A2, ERBB3, CHGB, SORCS1, COL28A1
Negative: TPBG, SLC7A8, WLS, FSTL1, HES1, ANO10, PAPPA2, CDH2, MSX1, SLC2A1
ZFP36L1, PIP5K1B, NFIA, TSC22D1, SLCO1C1, SOX2, PRNP, LINC00473, SPRY1, WIF1
NOV, COLEC12, PLCG2, GDF10, SPATS2L, RRBP1, BMP7, PAG1, WFIKKN2, RFX4
PC_ 4
Positive: EOMES, MGAT4C, ELAVL3, LHX1, RASGRP1, ADCYAP1, ELAVL4, SLC16A12, CELF4, TSHZ2
PTPRO, KCNK1, SCN9A, RELN, EPS8, RAB3B, SLIT1, GRID2, ASCL1, KRT222
ZNF385D, DCLK1, BDNF, ELAVL2, RGMB, PLCXD3, UNC5D, RALYL, PPP1R14C, DNER
Negative: PCAT4, TPH1, IMPG2, BCAT1, SST, FAM19A4, BTBD8, ETV3L, GSG1, PLPPR4
IL15, GABRG2, PDE6H, OLFM3, GNB3, CLSTN2, CRABP2, RBFOX1, AC007349.2, LINC02208
AIPL1, RD3, KCNH5, NCKAP5, PRKG2, AANAT, LRRC39, ANO2, ISOC1, AP000459.1
PC_ 5
Positive: PTN, PTPRZ1, SPARCL1, MEGF10, ESM1, DLK1, GABBR2, ATP1A2, NRIP3, GDF10
NELL2, SOX2, CBLN1, APCDD1, SYTL4, SERPINI1, ARHGAP26, PTGDS, VIPR2, FTL
MARCKS, GNG11, TRH, IL17RD, EPHB1, RBP4, RSPO2, APOE, OGFRL1, AKR1C1
Negative: CYP1B1, CP, ECEL1, CXCL14, IGFBP3, WIF1, WFIKKN2, MALAT1, FHIT, EPAS1
SLC4A10, EMX2, PAPPA2, TRPM3, EFEMP1, BMP4, MGP, KCNJ13, ID1, EXPH5
KRT18, KRT8, FBLN1, MSX1, FOS, GPNMB, DCN, CDC42EP3, COL6A3, SERPINF1 Idents(seu) <- 'orig.ident'
plot <- DimPlot(seu, reduction = "pca")
plot3 <- ElbowPlot(seu,ndims = 50)
plot3
plot2Warning: Transformation introduced infinite values in continuous x-axis
Warning: Removed 13701 rows containing missing values (geom_point).
plot
NA
NA
NA
# umap
seu <- RunUMAP(seu, reduction = "pca", n.neighbors = 43, dims = 1:25)Warning: The default method for RunUMAP has changed from calling Python UMAP via reticulate to the R-native UWOT using the cosine metric
To use Python UMAP via reticulate, set umap.method to 'umap-learn' and metric to 'correlation'
This message will be shown once per session
08:59:48 UMAP embedding parameters a = 0.9922 b = 1.112
08:59:48 Read 1833 rows and found 25 numeric columns
08:59:48 Using Annoy for neighbor search, n_neighbors = 43
08:59:48 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
08:59:48 Writing NN index file to temp file /var/folders/k4/khtkczkd5tn732ftjpwgtr240000gn/T//RtmpioYRC5/file1726b7ea878e1
08:59:48 Searching Annoy index using 1 thread, search_k = 4300
08:59:48 Annoy recall = 100%
08:59:49 Commencing smooth kNN distance calibration using 1 thread with target n_neighbors = 43
08:59:49 Initializing from normalized Laplacian + noise (using irlba)
08:59:49 Commencing optimization for 500 epochs, with 107418 positive edges
Using method 'umap'
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
08:59:53 Optimization finishedDimPlot(seu, reduction = "umap", group.by = "orig.ident")
NA
NA
NAMake the clusters Neurons1
seu <- FindNeighbors(seu, dims = 1:25, k.param = 43)Computing nearest neighbor graph
Computing SNNseu <- FindClusters(seu, resolution = c(0,0.2,0.25,0.5,0.8))Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 1833
Number of edges: 146997
Running Louvain algorithm...0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|Maximum modularity in 10 random starts: 1.0000
Number of communities: 1
Elapsed time: 0 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 1833
Number of edges: 146997
Running Louvain algorithm...0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|Maximum modularity in 10 random starts: 0.8606
Number of communities: 4
Elapsed time: 0 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 1833
Number of edges: 146997
Running Louvain algorithm...0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|Maximum modularity in 10 random starts: 0.8446
Number of communities: 5
Elapsed time: 0 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 1833
Number of edges: 146997
Running Louvain algorithm...0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|Maximum modularity in 10 random starts: 0.7787
Number of communities: 6
Elapsed time: 0 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 1833
Number of edges: 146997
Running Louvain algorithm...0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|Maximum modularity in 10 random starts: 0.7092
Number of communities: 8
Elapsed time: 0 secondsseu <- FindClusters(seu, resolution = c(1.2))Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 1833
Number of edges: 146997
Running Louvain algorithm...0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|Maximum modularity in 10 random starts: 0.6403
Number of communities: 10
Elapsed time: 0 secondslibrary(clustree)Loading required package: ggraph
Attaching package: ‘ggraph’
The following object is masked from ‘package:sp’:
geometryclustree(seu, prefix = "RNA_snn_res.")
DimPlot(seu)
# look a lot at the clusers
VlnPlot(seu, features = c("nFeature_RNA", "nCount_RNA", "percent.mt"), group.by = 'seurat_clusters', ncol = 1)
# these cells might be grouping by - how much MT and number of Features
# clusters 4,5,6 have more features
VlnPlot(seu, features = c("nFeature_RNA", "nCount_RNA", "percent.mt"), group.by = 'RNA_snn_res.0.25', ncol = 1)
# here cluster 3 has higher expression, cluster 1 and 4 have similar Features RNA
# cluster 0 has higher percent MT levels
VlnPlot(seu, features = c("nFeature_RNA", "nCount_RNA", "percent.mt"), group.by = 'RNA_snn_res.0.5', ncol = 1)
VlnPlot(seu, features = c("nFeature_RNA", "nCount_RNA", "percent.mt"), group.by = 'RNA_snn_res.0.2', ncol = 1)
# now only cluster 0 have high MT and low features, clusters 1,2,3 have simiular RNA and Counts
Find the cluster markers for Neurons1
Idents(seu) <- 'RNA_snn_res.0.2'
ClusterMarkers <- FindAllMarkers(seu)Calculating cluster 0
| | 0 % ~calculating
|+ | 1 % ~10s
|++ | 2 % ~09s
|++ | 3 % ~09s
|+++ | 4 % ~09s
|+++ | 5 % ~09s
|++++ | 6 % ~09s
|++++ | 7 % ~09s
|+++++ | 8 % ~08s
|+++++ | 9 % ~08s
|++++++ | 10% ~09s
|++++++ | 11% ~08s
|+++++++ | 12% ~08s
|+++++++ | 13% ~08s
|++++++++ | 14% ~08s
|++++++++ | 15% ~08s
|+++++++++ | 16% ~08s
|+++++++++ | 17% ~08s
|++++++++++ | 18% ~08s
|++++++++++ | 19% ~07s
|+++++++++++ | 20% ~07s
|+++++++++++ | 21% ~07s
|++++++++++++ | 22% ~07s
|++++++++++++ | 23% ~07s
|+++++++++++++ | 24% ~07s
|+++++++++++++ | 26% ~07s
|++++++++++++++ | 27% ~07s
|++++++++++++++ | 28% ~07s
|+++++++++++++++ | 29% ~07s
|+++++++++++++++ | 30% ~06s
|++++++++++++++++ | 31% ~06s
|++++++++++++++++ | 32% ~06s
|+++++++++++++++++ | 33% ~06s
|+++++++++++++++++ | 34% ~06s
|++++++++++++++++++ | 35% ~06s
|++++++++++++++++++ | 36% ~06s
|+++++++++++++++++++ | 37% ~06s
|+++++++++++++++++++ | 38% ~06s
|++++++++++++++++++++ | 39% ~06s
|++++++++++++++++++++ | 40% ~05s
|+++++++++++++++++++++ | 41% ~05s
|+++++++++++++++++++++ | 42% ~05s
|++++++++++++++++++++++ | 43% ~05s
|++++++++++++++++++++++ | 44% ~05s
|+++++++++++++++++++++++ | 45% ~05s
|+++++++++++++++++++++++ | 46% ~05s
|++++++++++++++++++++++++ | 47% ~05s
|++++++++++++++++++++++++ | 48% ~05s
|+++++++++++++++++++++++++ | 49% ~05s
|+++++++++++++++++++++++++ | 50% ~05s
|++++++++++++++++++++++++++ | 51% ~04s
|+++++++++++++++++++++++++++ | 52% ~04s
|+++++++++++++++++++++++++++ | 53% ~04s
|++++++++++++++++++++++++++++ | 54% ~04s
|++++++++++++++++++++++++++++ | 55% ~04s
|+++++++++++++++++++++++++++++ | 56% ~04s
|+++++++++++++++++++++++++++++ | 57% ~04s
|++++++++++++++++++++++++++++++ | 58% ~04s
|++++++++++++++++++++++++++++++ | 59% ~04s
|+++++++++++++++++++++++++++++++ | 60% ~04s
|+++++++++++++++++++++++++++++++ | 61% ~04s
|++++++++++++++++++++++++++++++++ | 62% ~03s
|++++++++++++++++++++++++++++++++ | 63% ~03s
|+++++++++++++++++++++++++++++++++ | 64% ~03s
|+++++++++++++++++++++++++++++++++ | 65% ~03s
|++++++++++++++++++++++++++++++++++ | 66% ~03s
|++++++++++++++++++++++++++++++++++ | 67% ~03s
|+++++++++++++++++++++++++++++++++++ | 68% ~03s
|+++++++++++++++++++++++++++++++++++ | 69% ~03s
|++++++++++++++++++++++++++++++++++++ | 70% ~03s
|++++++++++++++++++++++++++++++++++++ | 71% ~03s
|+++++++++++++++++++++++++++++++++++++ | 72% ~02s
|+++++++++++++++++++++++++++++++++++++ | 73% ~02s
|++++++++++++++++++++++++++++++++++++++ | 74% ~02s
|++++++++++++++++++++++++++++++++++++++ | 76% ~02s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~02s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~02s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~02s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~02s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~02s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~02s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~02s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~01s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~01s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~01s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~01s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=09s Calculating cluster 1
| | 0 % ~calculating
|+ | 1 % ~08s
|++ | 2 % ~08s
|++ | 3 % ~08s
|+++ | 4 % ~08s
|+++ | 5 % ~07s
|++++ | 6 % ~07s
|++++ | 7 % ~07s
|+++++ | 8 % ~07s
|+++++ | 9 % ~07s
|++++++ | 10% ~07s
|++++++ | 11% ~07s
|+++++++ | 12% ~07s
|+++++++ | 13% ~07s
|++++++++ | 14% ~07s
|++++++++ | 15% ~07s
|+++++++++ | 16% ~06s
|+++++++++ | 18% ~06s
|++++++++++ | 19% ~06s
|++++++++++ | 20% ~06s
|+++++++++++ | 21% ~06s
|+++++++++++ | 22% ~06s
|++++++++++++ | 23% ~06s
|++++++++++++ | 24% ~06s
|+++++++++++++ | 25% ~06s
|+++++++++++++ | 26% ~06s
|++++++++++++++ | 27% ~06s
|++++++++++++++ | 28% ~06s
|+++++++++++++++ | 29% ~05s
|+++++++++++++++ | 30% ~05s
|++++++++++++++++ | 31% ~05s
|++++++++++++++++ | 32% ~05s
|+++++++++++++++++ | 33% ~05s
|++++++++++++++++++ | 34% ~05s
|++++++++++++++++++ | 35% ~05s
|+++++++++++++++++++ | 36% ~05s
|+++++++++++++++++++ | 37% ~05s
|++++++++++++++++++++ | 38% ~05s
|++++++++++++++++++++ | 39% ~05s
|+++++++++++++++++++++ | 40% ~05s
|+++++++++++++++++++++ | 41% ~05s
|++++++++++++++++++++++ | 42% ~04s
|++++++++++++++++++++++ | 43% ~04s
|+++++++++++++++++++++++ | 44% ~04s
|+++++++++++++++++++++++ | 45% ~04s
|++++++++++++++++++++++++ | 46% ~04s
|++++++++++++++++++++++++ | 47% ~04s
|+++++++++++++++++++++++++ | 48% ~04s
|+++++++++++++++++++++++++ | 49% ~04s
|++++++++++++++++++++++++++ | 51% ~04s
|++++++++++++++++++++++++++ | 52% ~04s
|+++++++++++++++++++++++++++ | 53% ~04s
|+++++++++++++++++++++++++++ | 54% ~04s
|++++++++++++++++++++++++++++ | 55% ~04s
|++++++++++++++++++++++++++++ | 56% ~03s
|+++++++++++++++++++++++++++++ | 57% ~03s
|+++++++++++++++++++++++++++++ | 58% ~03s
|++++++++++++++++++++++++++++++ | 59% ~03s
|++++++++++++++++++++++++++++++ | 60% ~03s
|+++++++++++++++++++++++++++++++ | 61% ~03s
|+++++++++++++++++++++++++++++++ | 62% ~03s
|++++++++++++++++++++++++++++++++ | 63% ~03s
|++++++++++++++++++++++++++++++++ | 64% ~03s
|+++++++++++++++++++++++++++++++++ | 65% ~03s
|+++++++++++++++++++++++++++++++++ | 66% ~03s
|++++++++++++++++++++++++++++++++++ | 67% ~03s
|+++++++++++++++++++++++++++++++++++ | 68% ~02s
|+++++++++++++++++++++++++++++++++++ | 69% ~02s
|++++++++++++++++++++++++++++++++++++ | 70% ~02s
|++++++++++++++++++++++++++++++++++++ | 71% ~02s
|+++++++++++++++++++++++++++++++++++++ | 72% ~02s
|+++++++++++++++++++++++++++++++++++++ | 73% ~02s
|++++++++++++++++++++++++++++++++++++++ | 74% ~02s
|++++++++++++++++++++++++++++++++++++++ | 75% ~02s
|+++++++++++++++++++++++++++++++++++++++ | 76% ~02s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~02s
|++++++++++++++++++++++++++++++++++++++++ | 78% ~02s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~02s
|+++++++++++++++++++++++++++++++++++++++++ | 80% ~02s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~01s
|++++++++++++++++++++++++++++++++++++++++++ | 82% ~01s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~01s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~01s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~01s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~01s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=08s Calculating cluster 2
| | 0 % ~calculating
|+ | 1 % ~06s
|++ | 2 % ~06s
|++ | 3 % ~05s
|+++ | 4 % ~05s
|+++ | 5 % ~05s
|++++ | 6 % ~05s
|++++ | 8 % ~05s
|+++++ | 9 % ~05s
|+++++ | 10% ~05s
|++++++ | 11% ~05s
|++++++ | 12% ~05s
|+++++++ | 13% ~05s
|+++++++ | 14% ~05s
|++++++++ | 15% ~05s
|+++++++++ | 16% ~05s
|+++++++++ | 17% ~04s
|++++++++++ | 18% ~04s
|++++++++++ | 19% ~04s
|+++++++++++ | 20% ~04s
|+++++++++++ | 22% ~04s
|++++++++++++ | 23% ~04s
|++++++++++++ | 24% ~04s
|+++++++++++++ | 25% ~04s
|+++++++++++++ | 26% ~04s
|++++++++++++++ | 27% ~04s
|++++++++++++++ | 28% ~04s
|+++++++++++++++ | 29% ~04s
|++++++++++++++++ | 30% ~04s
|++++++++++++++++ | 31% ~04s
|+++++++++++++++++ | 32% ~04s
|+++++++++++++++++ | 33% ~04s
|++++++++++++++++++ | 34% ~04s
|++++++++++++++++++ | 35% ~04s
|+++++++++++++++++++ | 37% ~03s
|+++++++++++++++++++ | 38% ~03s
|++++++++++++++++++++ | 39% ~03s
|++++++++++++++++++++ | 40% ~03s
|+++++++++++++++++++++ | 41% ~03s
|+++++++++++++++++++++ | 42% ~03s
|++++++++++++++++++++++ | 43% ~03s
|+++++++++++++++++++++++ | 44% ~03s
|+++++++++++++++++++++++ | 45% ~03s
|++++++++++++++++++++++++ | 46% ~03s
|++++++++++++++++++++++++ | 47% ~03s
|+++++++++++++++++++++++++ | 48% ~03s
|+++++++++++++++++++++++++ | 49% ~03s
|++++++++++++++++++++++++++ | 51% ~03s
|++++++++++++++++++++++++++ | 52% ~03s
|+++++++++++++++++++++++++++ | 53% ~03s
|+++++++++++++++++++++++++++ | 54% ~03s
|++++++++++++++++++++++++++++ | 55% ~02s
|++++++++++++++++++++++++++++ | 56% ~02s
|+++++++++++++++++++++++++++++ | 57% ~02s
|++++++++++++++++++++++++++++++ | 58% ~02s
|++++++++++++++++++++++++++++++ | 59% ~02s
|+++++++++++++++++++++++++++++++ | 60% ~02s
|+++++++++++++++++++++++++++++++ | 61% ~02s
|++++++++++++++++++++++++++++++++ | 62% ~02s
|++++++++++++++++++++++++++++++++ | 63% ~02s
|+++++++++++++++++++++++++++++++++ | 65% ~02s
|+++++++++++++++++++++++++++++++++ | 66% ~02s
|++++++++++++++++++++++++++++++++++ | 67% ~02s
|++++++++++++++++++++++++++++++++++ | 68% ~02s
|+++++++++++++++++++++++++++++++++++ | 69% ~02s
|+++++++++++++++++++++++++++++++++++ | 70% ~02s
|++++++++++++++++++++++++++++++++++++ | 71% ~02s
|+++++++++++++++++++++++++++++++++++++ | 72% ~02s
|+++++++++++++++++++++++++++++++++++++ | 73% ~01s
|++++++++++++++++++++++++++++++++++++++ | 74% ~01s
|++++++++++++++++++++++++++++++++++++++ | 75% ~01s
|+++++++++++++++++++++++++++++++++++++++ | 76% ~01s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~01s
|++++++++++++++++++++++++++++++++++++++++ | 78% ~01s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~01s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~01s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~01s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~01s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~01s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~01s
|++++++++++++++++++++++++++++++++++++++++++++ | 86% ~01s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++ | 88% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++ | 90% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=06s Calculating cluster 3
| | 0 % ~calculating
|+ | 1 % ~09s
|++ | 2 % ~09s
|++ | 3 % ~09s
|+++ | 4 % ~09s
|+++ | 5 % ~09s
|++++ | 6 % ~09s
|++++ | 7 % ~08s
|+++++ | 8 % ~08s
|+++++ | 9 % ~09s
|++++++ | 10% ~09s
|++++++ | 11% ~08s
|+++++++ | 12% ~08s
|+++++++ | 13% ~08s
|++++++++ | 14% ~08s
|++++++++ | 15% ~08s
|+++++++++ | 16% ~08s
|+++++++++ | 18% ~08s
|++++++++++ | 19% ~07s
|++++++++++ | 20% ~07s
|+++++++++++ | 21% ~07s
|+++++++++++ | 22% ~07s
|++++++++++++ | 23% ~07s
|++++++++++++ | 24% ~07s
|+++++++++++++ | 25% ~07s
|+++++++++++++ | 26% ~07s
|++++++++++++++ | 27% ~07s
|++++++++++++++ | 28% ~06s
|+++++++++++++++ | 29% ~06s
|+++++++++++++++ | 30% ~06s
|++++++++++++++++ | 31% ~06s
|++++++++++++++++ | 32% ~06s
|+++++++++++++++++ | 33% ~06s
|++++++++++++++++++ | 34% ~06s
|++++++++++++++++++ | 35% ~06s
|+++++++++++++++++++ | 36% ~06s
|+++++++++++++++++++ | 37% ~06s
|++++++++++++++++++++ | 38% ~06s
|++++++++++++++++++++ | 39% ~05s
|+++++++++++++++++++++ | 40% ~05s
|+++++++++++++++++++++ | 41% ~05s
|++++++++++++++++++++++ | 42% ~05s
|++++++++++++++++++++++ | 43% ~05s
|+++++++++++++++++++++++ | 44% ~05s
|+++++++++++++++++++++++ | 45% ~05s
|++++++++++++++++++++++++ | 46% ~05s
|++++++++++++++++++++++++ | 47% ~05s
|+++++++++++++++++++++++++ | 48% ~05s
|+++++++++++++++++++++++++ | 49% ~04s
|++++++++++++++++++++++++++ | 51% ~04s
|++++++++++++++++++++++++++ | 52% ~04s
|+++++++++++++++++++++++++++ | 53% ~04s
|+++++++++++++++++++++++++++ | 54% ~04s
|++++++++++++++++++++++++++++ | 55% ~04s
|++++++++++++++++++++++++++++ | 56% ~04s
|+++++++++++++++++++++++++++++ | 57% ~04s
|+++++++++++++++++++++++++++++ | 58% ~04s
|++++++++++++++++++++++++++++++ | 59% ~04s
|++++++++++++++++++++++++++++++ | 60% ~04s
|+++++++++++++++++++++++++++++++ | 61% ~03s
|+++++++++++++++++++++++++++++++ | 62% ~03s
|++++++++++++++++++++++++++++++++ | 63% ~03s
|++++++++++++++++++++++++++++++++ | 64% ~03s
|+++++++++++++++++++++++++++++++++ | 65% ~03s
|+++++++++++++++++++++++++++++++++ | 66% ~03s
|++++++++++++++++++++++++++++++++++ | 67% ~03s
|+++++++++++++++++++++++++++++++++++ | 68% ~03s
|+++++++++++++++++++++++++++++++++++ | 69% ~03s
|++++++++++++++++++++++++++++++++++++ | 70% ~03s
|++++++++++++++++++++++++++++++++++++ | 71% ~03s
|+++++++++++++++++++++++++++++++++++++ | 72% ~02s
|+++++++++++++++++++++++++++++++++++++ | 73% ~02s
|++++++++++++++++++++++++++++++++++++++ | 74% ~02s
|++++++++++++++++++++++++++++++++++++++ | 75% ~02s
|+++++++++++++++++++++++++++++++++++++++ | 76% ~02s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~02s
|++++++++++++++++++++++++++++++++++++++++ | 78% ~02s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~02s
|+++++++++++++++++++++++++++++++++++++++++ | 80% ~02s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~02s
|++++++++++++++++++++++++++++++++++++++++++ | 82% ~02s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~01s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~01s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~01s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~01s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=09s top5 <- ClusterMarkers %>% group_by(cluster) %>% top_n(n=5, wt = avg_log2FC)
DoHeatmap(seu, features = top5$gene, size=3, angle =90, group.bar.height = 0.02, group.by = 'RNA_snn_res.0.2')
#write.csv(ClusterMarkers, "/Users/rhalenathomas/Documents/Data/scRNAseq/PhenoID/scRNAseqSorted/ClusterMarkers_neurons1_res025.csv")
write.csv(ClusterMarkers, "/Users/rhalenathomas/Documents/Data/scRNAseq/PhenoID/scRNAseqSorted/ClusterMarkers_neurons1_res02.csv")
DimPlot(seu, group.by = 'RNA_snn_res.0.2', reduction = 'umap')
Get the most highly expressed genes in the total data (Neurons1)
Filters out specific genes
seu.ft <- seu[!grepl("MALAT1", rownames(seu)), ]
seu.ft <- seu.ft[!grepl("^MT-", rownames(seu.ft)), ]
Try to find doublets with doublet finder
remotes::install_github('chris-mcginnis-ucsf/DoubletFinder')Skipping install of 'DoubletFinder' from a github remote, the SHA1 (67fb8b58) has not changed since last install.
Use `force = TRUE` to force installationsuppressMessages(require(DoubletFinder))
Do the double cells have more genes than the singlet??
VlnPlot(seu.d, features = "nFeature_RNA", group.by = DF.name, pt.size = 0.1)
NA
NAremove the doublets
dim(seu.d)
[1] 33538 1723dim(seu)[1] 33538 1833Save the filtered, doublet removed Neurons object Re-run PCA for clustering
saveRDS(seu.d, "/Users/rhalenathomas/Documents/Data/scRNAseq/PhenoID/scRNAseqSorted/objs/NeuronsFilteredSeu28092022.RDS")
DAsubgroups data has not be re-processed Run standard workflow chunk
seu <- AIW60
seu <- NormalizeData(seu, normalization.method = "LogNormalize", scale.factor = 10000)
Performing log-normalization
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|seu <- FindVariableFeatures(seu, selection.method = "vst", nfeatures = 2000)Calculating gene variances
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating feature variances of standardized and clipped values
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|seu <- ScaleData(seu)Centering and scaling data matrix
|
| | 0%
|
|================================================ | 50%
|
|================================================================================================| 100%seu <- RunPCA(seu)PC_ 1
Positive: STMN2, DCX, INA, MAP2, SOX4, KIF5C, NCAM1, GAP43, SOX11, NSG2
ANK3, GPM6A, SYT1, TUBB2A, NRXN1, PKIA, NR2F1, STMN4, UCHL1, RTN1
TAGLN3, RUNX1T1, DPYSL3, NSG1, NEFM, PGM2L1, PRKAR2B, PBX1, POU2F2, ELAVL4
Negative: SPARC, ZFP36L1, VIM, TPBG, MDK, ANXA5, GNG5, CD9, CA2, CAST
ANXA2, HES1, FSTL1, NFIA, CD99, IGFBP2, TTR, IGFBP7, GSTP1, ID1
ID3, CYSTM1, PLTP, ZFP36L2, TRPM3, FAT1, COLEC12, B2M, SPARCL1, TMEM123
PC_ 2
Positive: TTR, TPBG, IGFBP7, ANXA2, TRPM3, SLC7A8, CD9, SPINT2, CHCHD2, NFIA
SPARCL1, COLEC12, PPIC, PRNP, WFIKKN2, PIFO, BMP4, DMKN, LINC01088, ID1
MITF, CA2, ECEL1, SLC5A3, SERPINF1, CFAP126, PCP4, KRT18, SELENOP, CPVL
Negative: NUSAP1, TOP2A, MKI67, CENPF, PBK, CDK1, TPX2, NUF2, UBE2C, BIRC5
MAD2L1, CCNA2, ASPM, NCAPG, SPC25, PCLAF, CENPU, PIMREG, NDC80, KNL1
SMC4, KIF15, DLGAP5, SGO1, CDCA2, CDCA8, MIS18BP1, RRM2, CENPE, KIF11
PC_ 3
Positive: TTYH1, PTPRZ1, NES, QKI, VIM, BOC, FGFBP3, HES5, SOX2, SLC1A3
RFX4, FAM181B, IGDCC3, FAM181A, EDNRB, LINC00461, PON2, RPS27L, VCAM1, CCND1
ARHGEF6, ZFP36L1, PLP1, JAG1, PCDH18, ITGB8, SMOC1, DLK1, TMEM38B, TFDP2
Negative: RTN1, STMN2, NSG2, GAP43, INA, PRKAR2B, MAPT, UCHL1, NRXN1, PKIA
TRPM3, IGFBP7, NSG1, C11orf88, NCAM1, SHTN1, DLGAP5, PCP4, SOBP, DCX
CFAP126, ANK3, TTR, NEK2, HIST1H4C, GPM6A, XPR1, CELF4, SPINT2, MITF
PC_ 4
Positive: C11orf88, CAPSL, C1orf194, FAM81B, AKAP14, FAM183A, CFAP126, C9orf24, RSPH1, TCTEX1D1
ROPN1L, PIFO, C5orf49, CFAP52, DAW1, ARMC3, CCDC170, FAM216B, EFCAB1, MAP3K19
CP, SPAG6, CFAP45, AL357093.2, TEKT1, ANKRD66, MORN5, PTPRC, DYNLRB2, CFAP299
Negative: CNTNAP2, SYT4, ZIC1, APP, ZIC2, CBLN1, TNC, APCDD1, PTN, EPHA7
PAX6, SLITRK6, DSP, SPARCL1, WLS, ZFHX4, RSPO2, ATP1A2, CDK6, TXNIP
IL17RD, TPBG, AMER2, GDF10, ZIC4, PCDH9, WNT2B, HES1, GAP43, ITGA6
PC_ 5
Positive: CALM1, TMSB4X, C11orf88, CKB, ACAT2, C1orf194, FGFBP3, C5orf49, HMGCS1, NR2F1
SCD, PTPRZ1, AKAP14, CAPSL, MSMO1, CFAP126, RPS2, IDI1, FAM81B, FDPS
FAM183A, PEG10, RSPH1, FDFT1, TUBA1B, GPM6B, ROPN1L, ARL4A, QKI, NTRK2
Negative: CCNB1, PLK1, UBE2C, BUB1, CDC20, ASPM, KIF20A, CENPA, CDCA8, DLGAP5
AURKA, KIF2C, CCNB2, NEK2, FAM83D, TTK, NUF2, PIF1, TPX2, KIF14
GTSE1, CDCA3, CDKN3, PIMREG, HMMR, CENPE, CDCA2, DEPDC1, CKAP2L, SGO2 seu <- RunUMAP(seu, reduction = "pca", n.neighbors = 123, dims = 1:30)Warning: The default method for RunUMAP has changed from calling Python UMAP via reticulate to the R-native UWOT using the cosine metric
To use Python UMAP via reticulate, set umap.method to 'umap-learn' and metric to 'correlation'
This message will be shown once per session
13:03:03 UMAP embedding parameters a = 0.9922 b = 1.112
13:03:03 Read 15339 rows and found 30 numeric columns
13:03:03 Using Annoy for neighbor search, n_neighbors = 123
13:03:03 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
13:03:04 Writing NN index file to temp file /var/folders/k4/khtkczkd5tn732ftjpwgtr240000gn/T//RtmphW80ig/file17bc7524a9002
13:03:04 Searching Annoy index using 1 thread, search_k = 12300
13:03:14 Annoy recall = 100%
13:03:14 Commencing smooth kNN distance calibration using 1 thread with target n_neighbors = 123
13:03:16 Initializing from normalized Laplacian + noise (using irlba)
13:03:17 Commencing optimization for 200 epochs, with 2087112 positive edges
Using method 'umap'
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
13:03:32 Optimization finishedDimPlot(seu, reduction = "umap")
Annotate clusters Use: Organoid data, public brain data (LaManno, Lake, Mascako)
top10 <- head(VariableFeatures(DAsubtypes.sub), 10)
# plot variable features with and without labels
plot1 <- VariableFeaturePlot(DAsubtypes.sub)
plot2 <- LabelPoints(plot = plot1, points = top10, repel = TRUE)
When using repel, set xnudge and ynudge to 0 for optimal resultsplot2Warning: Transformation introduced infinite values in continuous x-axis
Warning: Removed 9387 rows containing missing values (geom_point).
See how each looks on UMAP
DimPlot(seu.q, group.by = 'RNA_snn_res.1.2')
DimPlot(seu.q, group.by = 'RNA_snn_res.0.2')
DimPlot(seu.q, group.by = 'AIW60.pred')
DimPlot(seu.q, group.by = 'MBOAIW.pred')
DimPlot(seu.q, group.by = 'MBOAST23.pred')
NA
NAFindClusters(seu.q, resolution = c(0, 0.2,0.4,0.6))
Error in FindClusters.Seurat(seu.q, resolution = c(0, 0.2, 0.4, 0.6)) :
Provided graph.name not present in Seurat objectNARedo find clusters
seu.q <- FindNeighbors(seu.q, dims = 1:25, k.param = 43)Computing nearest neighbor graph
Computing SNNseu.q <- FindClusters(seu.q, resolution = c(0,0.2,0.4,0.6))Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 1723
Number of edges: 145289
Running Louvain algorithm...0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|Maximum modularity in 10 random starts: 1.0000
Number of communities: 1
Elapsed time: 0 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 1723
Number of edges: 145289
Running Louvain algorithm...0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|Maximum modularity in 10 random starts: 0.8573
Number of communities: 4
Elapsed time: 0 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 1723
Number of edges: 145289
Running Louvain algorithm...0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|Maximum modularity in 10 random starts: 0.7887
Number of communities: 6
Elapsed time: 0 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 1723
Number of edges: 145289
Running Louvain algorithm...0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|Maximum modularity in 10 random starts: 0.7394
Number of communities: 7
Elapsed time: 0 secondsseu.q <- FindClusters(seu.q, resolution = c(1.2))Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 1723
Number of edges: 145289
Running Louvain algorithm...0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|Maximum modularity in 10 random starts: 0.6259
Number of communities: 10
Elapsed time: 0 secondslibrary(clustree)Loading required package: ggraph
Attaching package: ‘ggraph’
The following object is masked from ‘package:sp’:
geometryclustree(seu.q, prefix = "RNA_snn_res.")
DimPlot(seu.q)
Look at the predictions in the new clusters
Based on the 3 different predictions I can lable the cell types
0 - NPC or early neurons 1 - immature excitatory neurons 2 - NPC or early neurons 3 - RG or Oligos 4- Dopaminergic neurons - possibly early 5 - NPC or early neurons 6 - Radial GLia
I will also find markers and look at a list of neuronal markers
top5 <- ClusterMarkers %>% group_by(cluster) %>% top_n(n=5, wt = avg_log2FC)
DoHeatmap(seu.q, features = top5$gene, size=3, angle =90, group.bar.height = 0.02, group.by = 'RNA_snn_res.0.6')
Warning in DoHeatmap(seu.q, features = top5$gene, size = 3, angle = 90, :
The following features were omitted as they were not found in the scale.data slot for the RNA assay: SAT1, MTRNR2L12, MT-ND2
write.csv(ClusterMarkers,"/Users/rhalenathomas/Documents/Data/scRNAseq/PhenoID/scRNAseqSorted/Neurons1ClusterMarkers7.csv")Explore some Gene expression levels
feature_list = c("MKI67","SOX2","POU5F1","DLX2","PAX6","SOX9","HES1","NES","RBFOX3","MAP2","NCAM1","CD24","GRIA2","GRIN2B","GABBR1","GAD1","GAD2","GABRA1","GABRB2","TH","ALDH1A1","LMX1B","NR4A2","CORIN","CALB1","KCNJ6","CXCR4","ITGA6","SLC1A3","CD44","AQP4","S100B", "PDGFRA","OLIG2","MBP","CLDN11","VIM","VCAM1")
DoHeatmap(seu.q, features = feature_list, size=3, angle =90, group.bar.height = 0.02, group.by = 'RNA_snn_res.0.6')Warning in DoHeatmap(seu.q, features = feature_list, size = 3, angle = 90, :
The following features were omitted as they were not found in the scale.data slot for the RNA assay: VCAM1, CLDN11, OLIG2, PDGFRA, CD44, SLC1A3, KCNJ6, CORIN, NR4A2, LMX1B, ALDH1A1, GABRA1, GAD2, GABBR1, NCAM1, RBFOX3, NES, SOX9, DLX2, POU5F1, MKI67
DotPlot(seu.q, features = feature_list) +RotatedAxis()
PD_poulin = c("TH","SLC6A3","SLC18A2","SOX6","NDNF","SNCG","ALDH1A1","CALB1","TACR2","SLC17A6","SLC32A1","OTX2","GRP","LPL","CCK","VIP")
DoHeatmap(seu.q, features = PD_poulin, size=3, angle =90, group.bar.height = 0.02, group.by = 'RNA_snn_res.0.6')Warning in DoHeatmap(seu.q, features = PD_poulin, size = 3, angle = 90, :
The following features were omitted as they were not found in the scale.data slot for the RNA assay: VIP, CCK, GRP, SLC32A1, TACR2, ALDH1A1, SNCG, NDNF, SOX6, SLC18A2, SLC6A3
DotPlot(seu.q, features = PD_poulin)+RotatedAxis()
ealryNeur = c("DCX","NEUROD1","TBR1")
proliferation = c("PCNA","MKI67")
neuralstem = c("SOX2","NES","PAX6","MASH1")
feature_list <- c("DCX","NEUROD1","TBR1","PCNA","MKI67","SOX2","NES","PAX6","MASH1")
DoHeatmap(seu.q, features = feature_list, size=3, angle =90, group.bar.height = 0.02, group.by = 'RNA_snn_res.0.6')Warning in DoHeatmap(seu.q, features = feature_list, size = 3, angle = 90, :
The following features were omitted as they were not found in the scale.data slot for the RNA assay: MASH1, NES, MKI67, PCNA
DotPlot(seu.q, features = feature_list)+RotatedAxis()Warning in FetchData.Seurat(object = object, vars = features, cells = cells) :
The following requested variables were not found: MASH1
# no proliferation marker expression PCNA or MKI67
# cluster 4 DA neurons - shows early neuron marker and low PAX 4
# cluster 3 has higher SOX2 - neuroblast marker / NPC marker
mat_neuron = c("RBFOX3","SYP","DLG45","VAMP1","VAMP2","TUBB3","SYT1","BSN","HOMER1","SLC17A6")
# NeuN is FOX3 - RBFOX3
# PSD95 also SP-90 or DLG4
# VGLUT2 is SLC17A6
DoHeatmap(seu.q, features = mat_neuron, size=3, angle =90, group.bar.height = 0.02, group.by = 'RNA_snn_res.0.6')Warning in DoHeatmap(seu.q, features = mat_neuron, size = 3, angle = 90, :
The following features were omitted as they were not found in the scale.data slot for the RNA assay: HOMER1, BSN, TUBB3, VAMP1, DLG45, SYP, RBFOX3
# cluster 4 also show mature neuron markers
DotPlot(seu.q, features = mat_neuron)+RotatedAxis()Warning in FetchData.Seurat(object = object, vars = features, cells = cells) :
The following requested variables were not found: DLG45
# excitatory neuron markers
ex = c("GRIA2","GRIA1","GRIA4","GRIN1","GRIN2B","GRIN2A","GRIN3A","GRIN3","GRIP1","CAMK2A")
DoHeatmap(seu.q, features = ex, size=3, angle =90, group.bar.height = 0.02, group.by = 'RNA_snn_res.0.6')Warning in DoHeatmap(seu.q, features = ex, size = 3, angle = 90, group.bar.height = 0.02, :
The following features were omitted as they were not found in the scale.data slot for the RNA assay: CAMK2A, GRIP1, GRIN3, GRIN3A, GRIN2A, GRIN1, GRIA4
DotPlot(seu.q, features = ex)+RotatedAxis()Warning in FetchData.Seurat(object = object, vars = features, cells = cells) :
The following requested variables were not found: GRIN3
# inhibitory neuron markers
inh = c("GAD1","GAD2", "GAT1","PVALB","GABR2","GABR1","GBRR1","GABRB2","GABRB1","GABRB3","GABRA6","GABRA1","GABRA4","TRAK2")
DoHeatmap(seu.q, features = inh, size=3, angle =90, group.bar.height = 0.02, group.by = 'RNA_snn_res.0.6')Warning in DoHeatmap(seu.q, features = inh, size = 3, angle = 90, group.bar.height = 0.02, :
The following features were omitted as they were not found in the scale.data slot for the RNA assay: TRAK2, GABRA1, GABRA6, GABRB3, GABRB1, GBRR1, GABR1, GABR2, PVALB, GAT1, GAD2
DotPlot(seu.q, features = inh)+RotatedAxis()Warning in FetchData.Seurat(object = object, vars = features, cells = cells) :
The following requested variables were not found: GAT1, GABR2, GABR1, GBRR1
# cluster 4 is more excitatory than inhbitory but neither marker set has much expression
Checkout the Enricher cell type libraries from
N1.c6 <- ClusterMarkers %>% filter(cluster == 6 & avg_log2FC > 0)
genes <- N1.c6$gene
N1.c6.Er <- enrichr(genes, databases = db)
Uploading data to Enrichr... Done.
Querying Allen_Brain_Atlas_up... Done.
Querying Descartes_Cell_Types_and_Tissue_2021... Done.
Querying CellMarker_Augmented_2021... Done.
Querying Azimuth_Cell_Types_2021... Done.
Parsing results... Done.plotEnrich(N1.c6.Er[[1]], showTerms = 20, numChar = 40, y = "Count", orderBy = "P.value")
plotEnrich(N1.c6.Er[[2]], showTerms = 20, numChar = 40, y = "Count", orderBy = "P.value")
plotEnrich(N1.c6.Er[[3]], showTerms = 20, numChar = 40, y = "Count", orderBy = "P.value")
plotEnrich(N1.c6.Er[[4]], showTerms = 20, numChar = 40, y = "Count", orderBy = "P.value")
N1.Er.genes.1 <- N1.c6.Er[[1]] %>% select(Term, Genes, Combined.Score)
N1.Er.genes.1
N1.Er.genes.2 <- N1.c6.Er[[2]] %>% select(Term, Genes, Combined.Score)
N1.Er.genes.2
N1.Er.genes.3 <- N1.c6.Er[[3]] %>% select(Term, Genes, Combined.Score)
N1.Er.genes.3
N1.Er.genes.4 <- N1.c6.Er[[4]] %>% select(Term, Genes, Combined.Score)
N1.Er.genes.4NALibrary of tissue cell types for up regulated genes per cluster 0 - hypothalmus, DA A13 1- neural plate, Radial Glia 2 - Neural stem 3 - stromal, astro OPC 4 - Neurons 5 - endothelial, pericyte 6 - maybe neurons maybe not
By the combined information - annotate the clusters in Neurons1
Idents(seu.q) <- 'RNA_snn_res.0.6'
cluster.ids <- c("ImmatureNeurons","Neurons","NPC","OPC-RG","DAneurons",
"Other","RG")
unique(seu.q$RNA_snn_res.0.6)
[1] 1 5 0 3 4 2 6
Levels: 0 1 2 3 4 5 6names(cluster.ids) <- levels(seu.q)
seu.q <- RenameIdents(seu.q, cluster.ids)
seu.q$subgroups <- Idents(seu.q)
DimPlot(seu.q, reduction = "umap", label = TRUE, group.by = 'subgroups', repel = TRUE)
saveRDS(seu.q, "/Users/rhalenathomas/Documents/Data/scRNAseq/PhenoID/scRNAseqSorted/objs/Neuron1LabledSeu30092022.RDS")
Next Repeat everything for Neurons2
seu.ft <- subset(seu, subset = nFeature_RNA > 300 & nCount_RNA > 500 & nCount_RNA < 10000)
seu.ft
An object of class Seurat
33541 features across 9657 samples within 2 assays
Active assay: RNA (33538 features, 0 variable features)
1 other assay present: HTODoublet finder
Remove the doublet cells
seu.d <- seu.d[, seu.d@meta.data[, DF.name]== "Singlet"]
dim(seu.d)[1] 33524 8884dim(seu)[1] 33538 34830# 9657 cells pre filter
Repeat workflow with doublet removed data and find clusters for
clustree(seu.q)
DimPlot(seu.q, reduction = "umap", group.by = 'RNA_snn_res.0.2')
DimPlot(seu.q, reduction = "umap", group.by = 'RNA_snn_res.0.4')
DimPlot(seu.q, reduction = "umap", group.by = 'RNA_snn_res.0.6')
DimPlot(seu.q, reduction = "umap", group.by = 'RNA_snn_res.1.2')
Label cell types using the label transfer
# SNCA and control midbrain organoids 165 days in culture
MBO <- readRDS("/Users/rhalenathomas/Documents/Data/scRNAseq/AST23_BrainComm/MBOclusters_names29072021.rds")
# Midbrain AIW002 120 days in culture
AIWMBO <- readRDS("/Users/rhalenathomas/Documents/Data/scRNAseq/AIWtrio120days/MOintegratedClusterK123res0.8.names_nov16_2021")
# Midbrain AIW002 60 days in culture
AIW60 <- readRDS("/Users/rhalenathomas/Documents/Data/scRNAseq/AIWtrio60days/AWI002ParkinKOPinkKO60days_labels_14052022.rds")
# query
#seu.q <- readRDS("/Users/rhalenathomas/Documents/Data/scRNAseq/PhenoID/scRNAseqSorted/objs/NeuronsFilteredSeu28092022.RDS")
#first predict with the MBO data
Idents(MBO) <- "cluster_labels"
DefaultAssay(MBO) <- "RNA"
# find the reference anchors
print("finding reference anchors")[1] "finding reference anchors"anchors <- FindTransferAnchors(reference = MBO ,query = seu.q, dims = 1:25)Performing PCA on the provided reference using 1997 features as input.
Projecting cell embeddings
Finding neighborhoods
Finding anchors
Found 1228 anchors
Filtering anchors
Retained 528 anchors
as(<ngCMatrix>, "dgCMatrix") is deprecated since Matrix 1.5-0; do as(., "dMatrix") insteadprint("getting predictions")[1] "getting predictions"predictions <- TransferData(anchorset = anchors, refdata = MBO$cluster_labels)Finding integration vectors
Finding integration vector weights
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Predicting cell labelsseu.q <- AddMetaData(seu.q, metadata = predictions)
print(table(seu.q$predicted.id))
Epithelial Neural Precursors Neurons-DA Neurons-e
9 191 347 602
Oligodendrocytes RGa RGd1 RGd2
7045 5 666 19 Idents(seu.q) <- 'predicted.id'
# add new dataslot for MBO predicted ID to make the next prediction
seu.q$MBOAST23.pred <- Idents(seu.q)
DimPlot(seu.q, group.by = 'MBOAST23.pred', label = TRUE)
## check the proportion of cell types predicted in each cluster
t.lables <- as.data.frame(table(seu.q$RNA_snn_res.0.2, seu.q$predicted.id))
pr.t.lables <- as.data.frame(prop.table(table(seu.q$RNA_snn_res.0.2, seu.q$predicted.id)))
t.lables$Freq <- as.double(t.lables$Freq)
# try bar chart
ggplot(t.lables, aes(y = Freq, x = Var1, fill = Var2)) + geom_bar(position = "stack", stat= "identity")
# clusters don't break up by the predicted cell types
############ another predictions now using the AIW organoids
Idents(AIWMBO) <- "res08names"
DefaultAssay(AIWMBO) <- "RNA"
anchors <- FindTransferAnchors(reference = AIWMBO ,query = seu.q, dims = 1:25)Performing PCA on the provided reference using 2000 features as input.
Projecting cell embeddings
Finding neighborhoods
Finding anchors
Found 3590 anchors
Filtering anchors
Retained 1923 anchorsprint("getting predictions")[1] "getting predictions"predictions <- TransferData(anchorset = anchors, refdata = AIWMBO$res08names)Finding integration vectors
Finding integration vector weights
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Predicting cell labelsseu.q <- AddMetaData(seu.q, metadata = predictions)
print(table(seu.q$predicted.id))
epithelial Neurons_DA Neurons_early_inh OPC
37 492 5556 32
RGa RGd1 RGd3
2732 18 17 Idents(seu.q) <- 'predicted.id'
# add new dataslot for MBO predicted ID to make the next prediction
seu.q$MBOAIW.pred <- Idents(seu.q)
DimPlot(seu.q, group.by = 'MBOAIW.pred', label = TRUE)
## check the proportion of cell types predicted in each cluster
t.lables <- as.data.frame(table(seu.q$RNA_snn_res.0.2, seu.q$predicted.id))
pr.t.lables <- as.data.frame(prop.table(table(seu.q$RNA_snn_res.0.2, seu.q$predicted.id)))
t.lables$Freq <- as.double(t.lables$Freq)
# try bar chart
ggplot(t.lables, aes(y = Freq, x = Var1, fill = Var2)) + geom_bar(position = "stack", stat= "identity")
# the predicted cell types make more sense from the AIW002 organoid
# now predict with the AIW002 60 days organoid
Idents(AIW60) <- "cluster.ids"
DefaultAssay(AIW60) <- "RNA"
anchors <- FindTransferAnchors(reference = AIW60, query = seu.q, dims = 1:25)Performing PCA on the provided reference using 1999 features as input.
Projecting cell embeddings
Finding neighborhoods
Finding anchors
Found 3130 anchors
Filtering anchors
Retained 1215 anchorsprint("getting predictions")[1] "getting predictions"predictions <- TransferData(anchorset = anchors, refdata = AIW60$cluster.ids) Finding integration vectors
Finding integration vector weights
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Predicting cell labelsseu.q <- AddMetaData(seu.q, metadata = predictions)
print(table(seu.q$predicted.id))
Dopaminergic Neurons early 1 Neural Epithelial
950 15
Neural Precursors Radial Glia 1
6332 1572
Radial Glia 2
15 Idents(seu.q) <- 'predicted.id'
# add new dataslot for MBO predicted ID to make the next prediction
seu.q$AIW60.pred <- Idents(seu.q)
DimPlot(seu.q, group.by = 'AIW60.pred', label = TRUE)
## check the proportion of cell types predicted in each cluster
t.lables <- as.data.frame(table(seu.q$RNA_snn_res.0.2, seu.q$predicted.id))
pr.t.lables <- as.data.frame(prop.table(table(seu.q$RNA_snn_res.0.2, seu.q$predicted.id)))
t.lables$Freq <- as.double(t.lables$Freq)
# try bar chart
ggplot(t.lables, aes(y = Freq, x = Var1, fill = Var2)) + geom_bar(position = "stack", stat= "identity")
# save ojbect with predicitons
saveRDS(seu.q, "/Users/rhalenathomas/Documents/Data/scRNAseq/PhenoID/scRNAseqSorted/objs/Neurons2PredictionsSeu30092022.RDS")
See the top predictions for each cluster in Neurons2 res 06
What cell types are predicted across the 3 references
0 - Neurons early , NPC, neurons excitatory 1 - Neurons early, NPC 2 - Neurons early, NPC, neurons excitatory some DA neurons 3 - Oligo, RG, 4 - Excitatory neurons, NPC, early neurons 5 - DA neurons, early DA neurons 6 - neurons immature NPC 7 - DA neurons 8 - RG, oligo, OPC, NPC 9 - Radial Glia 10 - NPC, neurons, oligo 11 - NPC, neurons, oligo
Find cluster markers and see how those would annotate
Idents(seu.q) <- 'RNA_snn_res.0.6'
ClusterMarkers <- FindAllMarkers(seu.q, only.pos = TRUE)Calculating cluster 0
| | 0 % ~calculating
|++++++++ | 14% ~00s
|+++++++++++++++ | 29% ~00s
|++++++++++++++++++++++ | 43% ~00s
|+++++++++++++++++++++++++++++ | 57% ~00s
|++++++++++++++++++++++++++++++++++++ | 71% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 1
| | 0 % ~calculating
|+ | 1 % ~03s
|++ | 2 % ~03s
|++ | 4 % ~03s
|+++ | 5 % ~03s
|+++ | 6 % ~03s
|++++ | 7 % ~03s
|+++++ | 8 % ~03s
|+++++ | 9 % ~03s
|++++++ | 11% ~03s
|++++++ | 12% ~03s
|+++++++ | 13% ~03s
|++++++++ | 14% ~03s
|++++++++ | 15% ~02s
|+++++++++ | 16% ~02s
|+++++++++ | 18% ~02s
|++++++++++ | 19% ~02s
|++++++++++ | 20% ~02s
|+++++++++++ | 21% ~02s
|++++++++++++ | 22% ~02s
|++++++++++++ | 24% ~02s
|+++++++++++++ | 25% ~02s
|+++++++++++++ | 26% ~02s
|++++++++++++++ | 27% ~02s
|+++++++++++++++ | 28% ~02s
|+++++++++++++++ | 29% ~02s
|++++++++++++++++ | 31% ~02s
|++++++++++++++++ | 32% ~02s
|+++++++++++++++++ | 33% ~02s
|++++++++++++++++++ | 34% ~02s
|++++++++++++++++++ | 35% ~02s
|+++++++++++++++++++ | 36% ~02s
|+++++++++++++++++++ | 38% ~02s
|++++++++++++++++++++ | 39% ~02s
|++++++++++++++++++++ | 40% ~02s
|+++++++++++++++++++++ | 41% ~02s
|++++++++++++++++++++++ | 42% ~02s
|++++++++++++++++++++++ | 44% ~02s
|+++++++++++++++++++++++ | 45% ~02s
|+++++++++++++++++++++++ | 46% ~02s
|++++++++++++++++++++++++ | 47% ~02s
|+++++++++++++++++++++++++ | 48% ~02s
|+++++++++++++++++++++++++ | 49% ~02s
|++++++++++++++++++++++++++ | 51% ~02s
|++++++++++++++++++++++++++ | 52% ~02s
|+++++++++++++++++++++++++++ | 53% ~02s
|++++++++++++++++++++++++++++ | 54% ~01s
|++++++++++++++++++++++++++++ | 55% ~01s
|+++++++++++++++++++++++++++++ | 56% ~01s
|+++++++++++++++++++++++++++++ | 58% ~01s
|++++++++++++++++++++++++++++++ | 59% ~01s
|++++++++++++++++++++++++++++++ | 60% ~01s
|+++++++++++++++++++++++++++++++ | 61% ~01s
|++++++++++++++++++++++++++++++++ | 62% ~01s
|++++++++++++++++++++++++++++++++ | 64% ~01s
|+++++++++++++++++++++++++++++++++ | 65% ~01s
|+++++++++++++++++++++++++++++++++ | 66% ~01s
|++++++++++++++++++++++++++++++++++ | 67% ~01s
|+++++++++++++++++++++++++++++++++++ | 68% ~01s
|+++++++++++++++++++++++++++++++++++ | 69% ~01s
|++++++++++++++++++++++++++++++++++++ | 71% ~01s
|++++++++++++++++++++++++++++++++++++ | 72% ~01s
|+++++++++++++++++++++++++++++++++++++ | 73% ~01s
|++++++++++++++++++++++++++++++++++++++ | 74% ~01s
|++++++++++++++++++++++++++++++++++++++ | 75% ~01s
|+++++++++++++++++++++++++++++++++++++++ | 76% ~01s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~01s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~01s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~01s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~01s
|++++++++++++++++++++++++++++++++++++++++++ | 82% ~01s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~01s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~01s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 88% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=03s Calculating cluster 2
| | 0 % ~calculating
|+ | 1 % ~03s
|++ | 3 % ~03s
|++ | 4 % ~03s
|+++ | 5 % ~03s
|++++ | 7 % ~03s
|++++ | 8 % ~03s
|+++++ | 9 % ~03s
|++++++ | 11% ~03s
|++++++ | 12% ~03s
|+++++++ | 13% ~03s
|++++++++ | 15% ~02s
|++++++++ | 16% ~02s
|+++++++++ | 17% ~02s
|++++++++++ | 19% ~02s
|++++++++++ | 20% ~02s
|+++++++++++ | 21% ~02s
|++++++++++++ | 23% ~02s
|++++++++++++ | 24% ~02s
|+++++++++++++ | 25% ~02s
|++++++++++++++ | 27% ~02s
|++++++++++++++ | 28% ~02s
|+++++++++++++++ | 29% ~02s
|++++++++++++++++ | 31% ~02s
|++++++++++++++++ | 32% ~02s
|+++++++++++++++++ | 33% ~02s
|++++++++++++++++++ | 35% ~02s
|++++++++++++++++++ | 36% ~02s
|+++++++++++++++++++ | 37% ~02s
|++++++++++++++++++++ | 39% ~02s
|++++++++++++++++++++ | 40% ~02s
|+++++++++++++++++++++ | 41% ~02s
|++++++++++++++++++++++ | 43% ~02s
|++++++++++++++++++++++ | 44% ~02s
|+++++++++++++++++++++++ | 45% ~02s
|++++++++++++++++++++++++ | 47% ~01s
|++++++++++++++++++++++++ | 48% ~01s
|+++++++++++++++++++++++++ | 49% ~01s
|++++++++++++++++++++++++++ | 51% ~01s
|++++++++++++++++++++++++++ | 52% ~01s
|+++++++++++++++++++++++++++ | 53% ~01s
|++++++++++++++++++++++++++++ | 55% ~01s
|++++++++++++++++++++++++++++ | 56% ~01s
|+++++++++++++++++++++++++++++ | 57% ~01s
|++++++++++++++++++++++++++++++ | 59% ~01s
|++++++++++++++++++++++++++++++ | 60% ~01s
|+++++++++++++++++++++++++++++++ | 61% ~01s
|++++++++++++++++++++++++++++++++ | 63% ~01s
|++++++++++++++++++++++++++++++++ | 64% ~01s
|+++++++++++++++++++++++++++++++++ | 65% ~01s
|++++++++++++++++++++++++++++++++++ | 67% ~01s
|++++++++++++++++++++++++++++++++++ | 68% ~01s
|+++++++++++++++++++++++++++++++++++ | 69% ~01s
|++++++++++++++++++++++++++++++++++++ | 71% ~01s
|++++++++++++++++++++++++++++++++++++ | 72% ~01s
|+++++++++++++++++++++++++++++++++++++ | 73% ~01s
|++++++++++++++++++++++++++++++++++++++ | 75% ~01s
|++++++++++++++++++++++++++++++++++++++ | 76% ~01s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~01s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~01s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~01s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~01s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~00s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=03s Calculating cluster 3
| | 0 % ~calculating
|+ | 2 % ~02s
|++ | 3 % ~02s
|+++ | 5 % ~02s
|++++ | 6 % ~02s
|++++ | 8 % ~02s
|+++++ | 9 % ~02s
|++++++ | 11% ~02s
|+++++++ | 12% ~02s
|+++++++ | 14% ~02s
|++++++++ | 15% ~02s
|+++++++++ | 17% ~02s
|++++++++++ | 18% ~02s
|++++++++++ | 20% ~02s
|+++++++++++ | 21% ~02s
|++++++++++++ | 23% ~02s
|+++++++++++++ | 24% ~02s
|+++++++++++++ | 26% ~02s
|++++++++++++++ | 27% ~02s
|+++++++++++++++ | 29% ~02s
|++++++++++++++++ | 30% ~02s
|++++++++++++++++ | 32% ~02s
|+++++++++++++++++ | 33% ~02s
|++++++++++++++++++ | 35% ~02s
|+++++++++++++++++++ | 36% ~02s
|+++++++++++++++++++ | 38% ~02s
|++++++++++++++++++++ | 39% ~02s
|+++++++++++++++++++++ | 41% ~02s
|++++++++++++++++++++++ | 42% ~02s
|++++++++++++++++++++++ | 44% ~02s
|+++++++++++++++++++++++ | 45% ~02s
|++++++++++++++++++++++++ | 47% ~01s
|+++++++++++++++++++++++++ | 48% ~01s
|+++++++++++++++++++++++++ | 50% ~01s
|++++++++++++++++++++++++++ | 52% ~01s
|+++++++++++++++++++++++++++ | 53% ~01s
|++++++++++++++++++++++++++++ | 55% ~01s
|+++++++++++++++++++++++++++++ | 56% ~01s
|+++++++++++++++++++++++++++++ | 58% ~01s
|++++++++++++++++++++++++++++++ | 59% ~01s
|+++++++++++++++++++++++++++++++ | 61% ~01s
|++++++++++++++++++++++++++++++++ | 62% ~01s
|++++++++++++++++++++++++++++++++ | 64% ~01s
|+++++++++++++++++++++++++++++++++ | 65% ~01s
|++++++++++++++++++++++++++++++++++ | 67% ~01s
|+++++++++++++++++++++++++++++++++++ | 68% ~01s
|+++++++++++++++++++++++++++++++++++ | 70% ~01s
|++++++++++++++++++++++++++++++++++++ | 71% ~01s
|+++++++++++++++++++++++++++++++++++++ | 73% ~01s
|++++++++++++++++++++++++++++++++++++++ | 74% ~01s
|++++++++++++++++++++++++++++++++++++++ | 76% ~01s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~01s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~01s
|+++++++++++++++++++++++++++++++++++++++++ | 80% ~01s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~01s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 86% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 98% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=03s Calculating cluster 4
| | 0 % ~calculating
|+ | 1 % ~11s
|++ | 2 % ~10s
|++ | 3 % ~10s
|+++ | 4 % ~10s
|+++ | 5 % ~10s
|++++ | 7 % ~10s
|++++ | 8 % ~09s
|+++++ | 9 % ~09s
|+++++ | 10% ~09s
|++++++ | 11% ~09s
|+++++++ | 12% ~09s
|+++++++ | 13% ~09s
|++++++++ | 14% ~09s
|++++++++ | 15% ~09s
|+++++++++ | 16% ~09s
|+++++++++ | 18% ~09s
|++++++++++ | 19% ~09s
|++++++++++ | 20% ~08s
|+++++++++++ | 21% ~08s
|+++++++++++ | 22% ~08s
|++++++++++++ | 23% ~08s
|+++++++++++++ | 24% ~08s
|+++++++++++++ | 25% ~08s
|++++++++++++++ | 26% ~08s
|++++++++++++++ | 27% ~08s
|+++++++++++++++ | 29% ~08s
|+++++++++++++++ | 30% ~07s
|++++++++++++++++ | 31% ~07s
|++++++++++++++++ | 32% ~07s
|+++++++++++++++++ | 33% ~07s
|++++++++++++++++++ | 34% ~07s
|++++++++++++++++++ | 35% ~07s
|+++++++++++++++++++ | 36% ~07s
|+++++++++++++++++++ | 37% ~07s
|++++++++++++++++++++ | 38% ~07s
|++++++++++++++++++++ | 40% ~06s
|+++++++++++++++++++++ | 41% ~06s
|+++++++++++++++++++++ | 42% ~06s
|++++++++++++++++++++++ | 43% ~06s
|++++++++++++++++++++++ | 44% ~06s
|+++++++++++++++++++++++ | 45% ~06s
|++++++++++++++++++++++++ | 46% ~06s
|++++++++++++++++++++++++ | 47% ~06s
|+++++++++++++++++++++++++ | 48% ~06s
|+++++++++++++++++++++++++ | 49% ~05s
|++++++++++++++++++++++++++ | 51% ~05s
|++++++++++++++++++++++++++ | 52% ~05s
|+++++++++++++++++++++++++++ | 53% ~05s
|+++++++++++++++++++++++++++ | 54% ~05s
|++++++++++++++++++++++++++++ | 55% ~05s
|+++++++++++++++++++++++++++++ | 56% ~05s
|+++++++++++++++++++++++++++++ | 57% ~05s
|++++++++++++++++++++++++++++++ | 58% ~04s
|++++++++++++++++++++++++++++++ | 59% ~04s
|+++++++++++++++++++++++++++++++ | 60% ~04s
|+++++++++++++++++++++++++++++++ | 62% ~04s
|++++++++++++++++++++++++++++++++ | 63% ~04s
|++++++++++++++++++++++++++++++++ | 64% ~04s
|+++++++++++++++++++++++++++++++++ | 65% ~04s
|+++++++++++++++++++++++++++++++++ | 66% ~04s
|++++++++++++++++++++++++++++++++++ | 67% ~03s
|+++++++++++++++++++++++++++++++++++ | 68% ~03s
|+++++++++++++++++++++++++++++++++++ | 69% ~03s
|++++++++++++++++++++++++++++++++++++ | 70% ~03s
|++++++++++++++++++++++++++++++++++++ | 71% ~03s
|+++++++++++++++++++++++++++++++++++++ | 73% ~03s
|+++++++++++++++++++++++++++++++++++++ | 74% ~03s
|++++++++++++++++++++++++++++++++++++++ | 75% ~03s
|++++++++++++++++++++++++++++++++++++++ | 76% ~03s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~02s
|++++++++++++++++++++++++++++++++++++++++ | 78% ~02s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~02s
|+++++++++++++++++++++++++++++++++++++++++ | 80% ~02s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~02s
|++++++++++++++++++++++++++++++++++++++++++ | 82% ~02s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~02s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~02s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~02s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~01s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++ | 90% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=10s Calculating cluster 5
| | 0 % ~calculating
|+ | 1 % ~24s
|++ | 2 % ~24s
|++ | 3 % ~24s
|+++ | 4 % ~24s
|+++ | 5 % ~23s
|++++ | 6 % ~23s
|++++ | 7 % ~23s
|+++++ | 8 % ~23s
|+++++ | 9 % ~22s
|++++++ | 11% ~22s
|++++++ | 12% ~22s
|+++++++ | 13% ~22s
|+++++++ | 14% ~21s
|++++++++ | 15% ~21s
|++++++++ | 16% ~21s
|+++++++++ | 17% ~21s
|+++++++++ | 18% ~20s
|++++++++++ | 19% ~20s
|++++++++++ | 20% ~20s
|+++++++++++ | 21% ~20s
|++++++++++++ | 22% ~19s
|++++++++++++ | 23% ~19s
|+++++++++++++ | 24% ~19s
|+++++++++++++ | 25% ~19s
|++++++++++++++ | 26% ~18s
|++++++++++++++ | 27% ~18s
|+++++++++++++++ | 28% ~18s
|+++++++++++++++ | 29% ~18s
|++++++++++++++++ | 31% ~17s
|++++++++++++++++ | 32% ~17s
|+++++++++++++++++ | 33% ~17s
|+++++++++++++++++ | 34% ~16s
|++++++++++++++++++ | 35% ~16s
|++++++++++++++++++ | 36% ~16s
|+++++++++++++++++++ | 37% ~16s
|+++++++++++++++++++ | 38% ~15s
|++++++++++++++++++++ | 39% ~15s
|++++++++++++++++++++ | 40% ~15s
|+++++++++++++++++++++ | 41% ~15s
|++++++++++++++++++++++ | 42% ~14s
|++++++++++++++++++++++ | 43% ~14s
|+++++++++++++++++++++++ | 44% ~14s
|+++++++++++++++++++++++ | 45% ~14s
|++++++++++++++++++++++++ | 46% ~13s
|++++++++++++++++++++++++ | 47% ~13s
|+++++++++++++++++++++++++ | 48% ~13s
|+++++++++++++++++++++++++ | 49% ~13s
|++++++++++++++++++++++++++ | 51% ~12s
|++++++++++++++++++++++++++ | 52% ~12s
|+++++++++++++++++++++++++++ | 53% ~12s
|+++++++++++++++++++++++++++ | 54% ~12s
|++++++++++++++++++++++++++++ | 55% ~11s
|++++++++++++++++++++++++++++ | 56% ~11s
|+++++++++++++++++++++++++++++ | 57% ~11s
|+++++++++++++++++++++++++++++ | 58% ~10s
|++++++++++++++++++++++++++++++ | 59% ~10s
|++++++++++++++++++++++++++++++ | 60% ~10s
|+++++++++++++++++++++++++++++++ | 61% ~10s
|++++++++++++++++++++++++++++++++ | 62% ~09s
|++++++++++++++++++++++++++++++++ | 63% ~09s
|+++++++++++++++++++++++++++++++++ | 64% ~09s
|+++++++++++++++++++++++++++++++++ | 65% ~09s
|++++++++++++++++++++++++++++++++++ | 66% ~08s
|++++++++++++++++++++++++++++++++++ | 67% ~08s
|+++++++++++++++++++++++++++++++++++ | 68% ~08s
|+++++++++++++++++++++++++++++++++++ | 69% ~08s
|++++++++++++++++++++++++++++++++++++ | 71% ~07s
|++++++++++++++++++++++++++++++++++++ | 72% ~07s
|+++++++++++++++++++++++++++++++++++++ | 73% ~07s
|+++++++++++++++++++++++++++++++++++++ | 74% ~07s
|++++++++++++++++++++++++++++++++++++++ | 75% ~06s
|++++++++++++++++++++++++++++++++++++++ | 76% ~06s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~06s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~06s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~05s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~05s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~05s
|++++++++++++++++++++++++++++++++++++++++++ | 82% ~04s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~04s
|+++++++++++++++++++++++++++++++++++++++++++ | 84% ~04s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~04s
|++++++++++++++++++++++++++++++++++++++++++++ | 86% ~03s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~03s
|+++++++++++++++++++++++++++++++++++++++++++++ | 88% ~03s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~03s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~02s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~02s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~02s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~02s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=25s Calculating cluster 6
| | 0 % ~calculating
|+ | 1 % ~05s
|++ | 2 % ~04s
|++ | 4 % ~04s
|+++ | 5 % ~04s
|+++ | 6 % ~04s
|++++ | 7 % ~04s
|+++++ | 8 % ~04s
|+++++ | 9 % ~04s
|++++++ | 11% ~04s
|++++++ | 12% ~04s
|+++++++ | 13% ~04s
|++++++++ | 14% ~04s
|++++++++ | 15% ~04s
|+++++++++ | 16% ~03s
|+++++++++ | 18% ~03s
|++++++++++ | 19% ~03s
|++++++++++ | 20% ~03s
|+++++++++++ | 21% ~03s
|++++++++++++ | 22% ~03s
|++++++++++++ | 24% ~03s
|+++++++++++++ | 25% ~03s
|+++++++++++++ | 26% ~03s
|++++++++++++++ | 27% ~03s
|+++++++++++++++ | 28% ~03s
|+++++++++++++++ | 29% ~03s
|++++++++++++++++ | 31% ~03s
|++++++++++++++++ | 32% ~03s
|+++++++++++++++++ | 33% ~03s
|++++++++++++++++++ | 34% ~03s
|++++++++++++++++++ | 35% ~03s
|+++++++++++++++++++ | 36% ~03s
|+++++++++++++++++++ | 38% ~03s
|++++++++++++++++++++ | 39% ~03s
|++++++++++++++++++++ | 40% ~03s
|+++++++++++++++++++++ | 41% ~02s
|++++++++++++++++++++++ | 42% ~02s
|++++++++++++++++++++++ | 44% ~02s
|+++++++++++++++++++++++ | 45% ~02s
|+++++++++++++++++++++++ | 46% ~02s
|++++++++++++++++++++++++ | 47% ~02s
|+++++++++++++++++++++++++ | 48% ~02s
|+++++++++++++++++++++++++ | 49% ~02s
|++++++++++++++++++++++++++ | 51% ~02s
|++++++++++++++++++++++++++ | 52% ~02s
|+++++++++++++++++++++++++++ | 53% ~02s
|++++++++++++++++++++++++++++ | 54% ~02s
|++++++++++++++++++++++++++++ | 55% ~02s
|+++++++++++++++++++++++++++++ | 56% ~02s
|+++++++++++++++++++++++++++++ | 58% ~02s
|++++++++++++++++++++++++++++++ | 59% ~02s
|++++++++++++++++++++++++++++++ | 60% ~02s
|+++++++++++++++++++++++++++++++ | 61% ~02s
|++++++++++++++++++++++++++++++++ | 62% ~02s
|++++++++++++++++++++++++++++++++ | 64% ~02s
|+++++++++++++++++++++++++++++++++ | 65% ~01s
|+++++++++++++++++++++++++++++++++ | 66% ~01s
|++++++++++++++++++++++++++++++++++ | 67% ~01s
|+++++++++++++++++++++++++++++++++++ | 68% ~01s
|+++++++++++++++++++++++++++++++++++ | 69% ~01s
|++++++++++++++++++++++++++++++++++++ | 71% ~01s
|++++++++++++++++++++++++++++++++++++ | 72% ~01s
|+++++++++++++++++++++++++++++++++++++ | 73% ~01s
|++++++++++++++++++++++++++++++++++++++ | 74% ~01s
|++++++++++++++++++++++++++++++++++++++ | 75% ~01s
|+++++++++++++++++++++++++++++++++++++++ | 76% ~01s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~01s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~01s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~01s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~01s
|++++++++++++++++++++++++++++++++++++++++++ | 82% ~01s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~01s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~01s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~01s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++ | 88% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=04s Calculating cluster 7
| | 0 % ~calculating
|+ | 1 % ~32s
|++ | 2 % ~32s
|++ | 3 % ~32s
|+++ | 4 % ~32s
|+++ | 5 % ~31s
|++++ | 6 % ~31s
|++++ | 7 % ~31s
|+++++ | 8 % ~30s
|+++++ | 9 % ~30s
|++++++ | 10% ~30s
|++++++ | 11% ~29s
|+++++++ | 12% ~29s
|+++++++ | 14% ~29s
|++++++++ | 15% ~28s
|++++++++ | 16% ~28s
|+++++++++ | 17% ~27s
|+++++++++ | 18% ~27s
|++++++++++ | 19% ~27s
|++++++++++ | 20% ~26s
|+++++++++++ | 21% ~26s
|+++++++++++ | 22% ~26s
|++++++++++++ | 23% ~25s
|++++++++++++ | 24% ~25s
|+++++++++++++ | 25% ~25s
|++++++++++++++ | 26% ~24s
|++++++++++++++ | 27% ~24s
|+++++++++++++++ | 28% ~24s
|+++++++++++++++ | 29% ~24s
|++++++++++++++++ | 30% ~23s
|++++++++++++++++ | 31% ~23s
|+++++++++++++++++ | 32% ~22s
|+++++++++++++++++ | 33% ~22s
|++++++++++++++++++ | 34% ~22s
|++++++++++++++++++ | 35% ~21s
|+++++++++++++++++++ | 36% ~21s
|+++++++++++++++++++ | 38% ~21s
|++++++++++++++++++++ | 39% ~20s
|++++++++++++++++++++ | 40% ~20s
|+++++++++++++++++++++ | 41% ~20s
|+++++++++++++++++++++ | 42% ~19s
|++++++++++++++++++++++ | 43% ~19s
|++++++++++++++++++++++ | 44% ~18s
|+++++++++++++++++++++++ | 45% ~18s
|+++++++++++++++++++++++ | 46% ~18s
|++++++++++++++++++++++++ | 47% ~17s
|++++++++++++++++++++++++ | 48% ~17s
|+++++++++++++++++++++++++ | 49% ~17s
|+++++++++++++++++++++++++ | 50% ~16s
|++++++++++++++++++++++++++ | 51% ~16s
|+++++++++++++++++++++++++++ | 52% ~16s
|+++++++++++++++++++++++++++ | 53% ~15s
|++++++++++++++++++++++++++++ | 54% ~15s
|++++++++++++++++++++++++++++ | 55% ~15s
|+++++++++++++++++++++++++++++ | 56% ~14s
|+++++++++++++++++++++++++++++ | 57% ~14s
|++++++++++++++++++++++++++++++ | 58% ~14s
|++++++++++++++++++++++++++++++ | 59% ~13s
|+++++++++++++++++++++++++++++++ | 60% ~13s
|+++++++++++++++++++++++++++++++ | 61% ~13s
|++++++++++++++++++++++++++++++++ | 62% ~12s
|++++++++++++++++++++++++++++++++ | 64% ~12s
|+++++++++++++++++++++++++++++++++ | 65% ~11s
|+++++++++++++++++++++++++++++++++ | 66% ~11s
|++++++++++++++++++++++++++++++++++ | 67% ~11s
|++++++++++++++++++++++++++++++++++ | 68% ~10s
|+++++++++++++++++++++++++++++++++++ | 69% ~10s
|+++++++++++++++++++++++++++++++++++ | 70% ~10s
|++++++++++++++++++++++++++++++++++++ | 71% ~09s
|++++++++++++++++++++++++++++++++++++ | 72% ~09s
|+++++++++++++++++++++++++++++++++++++ | 73% ~09s
|+++++++++++++++++++++++++++++++++++++ | 74% ~08s
|++++++++++++++++++++++++++++++++++++++ | 75% ~08s
|+++++++++++++++++++++++++++++++++++++++ | 76% ~08s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~07s
|++++++++++++++++++++++++++++++++++++++++ | 78% ~07s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~07s
|+++++++++++++++++++++++++++++++++++++++++ | 80% ~06s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~06s
|++++++++++++++++++++++++++++++++++++++++++ | 82% ~06s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~05s
|+++++++++++++++++++++++++++++++++++++++++++ | 84% ~05s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~05s
|++++++++++++++++++++++++++++++++++++++++++++ | 86% ~04s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~04s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~04s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~03s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~03s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~03s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~02s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~02s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~02s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=32s Calculating cluster 8
| | 0 % ~calculating
|+ | 1 % ~10s
|++ | 2 % ~09s
|++ | 3 % ~09s
|+++ | 4 % ~09s
|+++ | 6 % ~09s
|++++ | 7 % ~09s
|++++ | 8 % ~09s
|+++++ | 9 % ~09s
|++++++ | 10% ~09s
|++++++ | 11% ~08s
|+++++++ | 12% ~08s
|+++++++ | 13% ~08s
|++++++++ | 15% ~08s
|++++++++ | 16% ~08s
|+++++++++ | 17% ~08s
|+++++++++ | 18% ~08s
|++++++++++ | 19% ~08s
|+++++++++++ | 20% ~08s
|+++++++++++ | 21% ~08s
|++++++++++++ | 22% ~07s
|++++++++++++ | 24% ~07s
|+++++++++++++ | 25% ~07s
|+++++++++++++ | 26% ~07s
|++++++++++++++ | 27% ~07s
|+++++++++++++++ | 28% ~07s
|+++++++++++++++ | 29% ~07s
|++++++++++++++++ | 30% ~07s
|++++++++++++++++ | 31% ~07s
|+++++++++++++++++ | 33% ~07s
|+++++++++++++++++ | 34% ~06s
|++++++++++++++++++ | 35% ~06s
|++++++++++++++++++ | 36% ~06s
|+++++++++++++++++++ | 37% ~06s
|++++++++++++++++++++ | 38% ~06s
|++++++++++++++++++++ | 39% ~06s
|+++++++++++++++++++++ | 40% ~06s
|+++++++++++++++++++++ | 42% ~06s
|++++++++++++++++++++++ | 43% ~06s
|++++++++++++++++++++++ | 44% ~05s
|+++++++++++++++++++++++ | 45% ~05s
|++++++++++++++++++++++++ | 46% ~05s
|++++++++++++++++++++++++ | 47% ~05s
|+++++++++++++++++++++++++ | 48% ~05s
|+++++++++++++++++++++++++ | 49% ~05s
|++++++++++++++++++++++++++ | 51% ~05s
|++++++++++++++++++++++++++ | 52% ~05s
|+++++++++++++++++++++++++++ | 53% ~05s
|+++++++++++++++++++++++++++ | 54% ~04s
|++++++++++++++++++++++++++++ | 55% ~04s
|+++++++++++++++++++++++++++++ | 56% ~04s
|+++++++++++++++++++++++++++++ | 57% ~04s
|++++++++++++++++++++++++++++++ | 58% ~04s
|++++++++++++++++++++++++++++++ | 60% ~04s
|+++++++++++++++++++++++++++++++ | 61% ~04s
|+++++++++++++++++++++++++++++++ | 62% ~04s
|++++++++++++++++++++++++++++++++ | 63% ~04s
|+++++++++++++++++++++++++++++++++ | 64% ~03s
|+++++++++++++++++++++++++++++++++ | 65% ~03s
|++++++++++++++++++++++++++++++++++ | 66% ~03s
|++++++++++++++++++++++++++++++++++ | 67% ~03s
|+++++++++++++++++++++++++++++++++++ | 69% ~03s
|+++++++++++++++++++++++++++++++++++ | 70% ~03s
|++++++++++++++++++++++++++++++++++++ | 71% ~03s
|++++++++++++++++++++++++++++++++++++ | 72% ~03s
|+++++++++++++++++++++++++++++++++++++ | 73% ~03s
|++++++++++++++++++++++++++++++++++++++ | 74% ~03s
|++++++++++++++++++++++++++++++++++++++ | 75% ~02s
|+++++++++++++++++++++++++++++++++++++++ | 76% ~02s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~02s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~02s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~02s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~02s
|++++++++++++++++++++++++++++++++++++++++++ | 82% ~02s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~02s
|+++++++++++++++++++++++++++++++++++++++++++ | 84% ~02s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~01s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~01s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=10s Calculating cluster 9
| | 0 % ~calculating
|+ | 1 % ~14s
|++ | 2 % ~14s
|++ | 3 % ~14s
|+++ | 4 % ~14s
|+++ | 5 % ~13s
|++++ | 6 % ~13s
|++++ | 8 % ~13s
|+++++ | 9 % ~13s
|+++++ | 10% ~13s
|++++++ | 11% ~13s
|++++++ | 12% ~13s
|+++++++ | 13% ~12s
|+++++++ | 14% ~12s
|++++++++ | 15% ~12s
|+++++++++ | 16% ~12s
|+++++++++ | 17% ~12s
|++++++++++ | 18% ~12s
|++++++++++ | 19% ~12s
|+++++++++++ | 20% ~11s
|+++++++++++ | 22% ~11s
|++++++++++++ | 23% ~11s
|++++++++++++ | 24% ~11s
|+++++++++++++ | 25% ~11s
|+++++++++++++ | 26% ~11s
|++++++++++++++ | 27% ~10s
|++++++++++++++ | 28% ~10s
|+++++++++++++++ | 29% ~10s
|++++++++++++++++ | 30% ~10s
|++++++++++++++++ | 31% ~10s
|+++++++++++++++++ | 32% ~10s
|+++++++++++++++++ | 33% ~10s
|++++++++++++++++++ | 34% ~09s
|++++++++++++++++++ | 35% ~09s
|+++++++++++++++++++ | 37% ~09s
|+++++++++++++++++++ | 38% ~09s
|++++++++++++++++++++ | 39% ~09s
|++++++++++++++++++++ | 40% ~09s
|+++++++++++++++++++++ | 41% ~08s
|+++++++++++++++++++++ | 42% ~08s
|++++++++++++++++++++++ | 43% ~08s
|+++++++++++++++++++++++ | 44% ~08s
|+++++++++++++++++++++++ | 45% ~08s
|++++++++++++++++++++++++ | 46% ~08s
|++++++++++++++++++++++++ | 47% ~08s
|+++++++++++++++++++++++++ | 48% ~08s
|+++++++++++++++++++++++++ | 49% ~07s
|++++++++++++++++++++++++++ | 51% ~07s
|++++++++++++++++++++++++++ | 52% ~07s
|+++++++++++++++++++++++++++ | 53% ~07s
|+++++++++++++++++++++++++++ | 54% ~07s
|++++++++++++++++++++++++++++ | 55% ~07s
|++++++++++++++++++++++++++++ | 56% ~06s
|+++++++++++++++++++++++++++++ | 57% ~06s
|++++++++++++++++++++++++++++++ | 58% ~06s
|++++++++++++++++++++++++++++++ | 59% ~06s
|+++++++++++++++++++++++++++++++ | 60% ~06s
|+++++++++++++++++++++++++++++++ | 61% ~06s
|++++++++++++++++++++++++++++++++ | 62% ~05s
|++++++++++++++++++++++++++++++++ | 63% ~05s
|+++++++++++++++++++++++++++++++++ | 65% ~05s
|+++++++++++++++++++++++++++++++++ | 66% ~05s
|++++++++++++++++++++++++++++++++++ | 67% ~05s
|++++++++++++++++++++++++++++++++++ | 68% ~05s
|+++++++++++++++++++++++++++++++++++ | 69% ~04s
|+++++++++++++++++++++++++++++++++++ | 70% ~04s
|++++++++++++++++++++++++++++++++++++ | 71% ~04s
|+++++++++++++++++++++++++++++++++++++ | 72% ~04s
|+++++++++++++++++++++++++++++++++++++ | 73% ~04s
|++++++++++++++++++++++++++++++++++++++ | 74% ~04s
|++++++++++++++++++++++++++++++++++++++ | 75% ~04s
|+++++++++++++++++++++++++++++++++++++++ | 76% ~03s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~03s
|++++++++++++++++++++++++++++++++++++++++ | 78% ~03s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~03s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~03s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~03s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~02s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~02s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~02s
|++++++++++++++++++++++++++++++++++++++++++++ | 86% ~02s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~02s
|+++++++++++++++++++++++++++++++++++++++++++++ | 88% ~02s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~02s
|++++++++++++++++++++++++++++++++++++++++++++++ | 90% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=14s Calculating cluster 10
| | 0 % ~calculating
|+ | 1 % ~08s
|++ | 2 % ~07s
|++ | 3 % ~08s
|+++ | 5 % ~07s
|+++ | 6 % ~07s
|++++ | 7 % ~07s
|+++++ | 8 % ~07s
|+++++ | 9 % ~07s
|++++++ | 10% ~07s
|++++++ | 11% ~07s
|+++++++ | 13% ~06s
|+++++++ | 14% ~06s
|++++++++ | 15% ~06s
|+++++++++ | 16% ~06s
|+++++++++ | 17% ~06s
|++++++++++ | 18% ~06s
|++++++++++ | 20% ~06s
|+++++++++++ | 21% ~06s
|+++++++++++ | 22% ~06s
|++++++++++++ | 23% ~06s
|+++++++++++++ | 24% ~06s
|+++++++++++++ | 25% ~06s
|++++++++++++++ | 26% ~05s
|++++++++++++++ | 28% ~05s
|+++++++++++++++ | 29% ~05s
|+++++++++++++++ | 30% ~05s
|++++++++++++++++ | 31% ~05s
|+++++++++++++++++ | 32% ~05s
|+++++++++++++++++ | 33% ~05s
|++++++++++++++++++ | 34% ~05s
|++++++++++++++++++ | 36% ~05s
|+++++++++++++++++++ | 37% ~05s
|+++++++++++++++++++ | 38% ~05s
|++++++++++++++++++++ | 39% ~05s
|+++++++++++++++++++++ | 40% ~04s
|+++++++++++++++++++++ | 41% ~04s
|++++++++++++++++++++++ | 43% ~04s
|++++++++++++++++++++++ | 44% ~04s
|+++++++++++++++++++++++ | 45% ~04s
|+++++++++++++++++++++++ | 46% ~04s
|++++++++++++++++++++++++ | 47% ~04s
|+++++++++++++++++++++++++ | 48% ~04s
|+++++++++++++++++++++++++ | 49% ~04s
|++++++++++++++++++++++++++ | 51% ~04s
|++++++++++++++++++++++++++ | 52% ~04s
|+++++++++++++++++++++++++++ | 53% ~04s
|++++++++++++++++++++++++++++ | 54% ~03s
|++++++++++++++++++++++++++++ | 55% ~03s
|+++++++++++++++++++++++++++++ | 56% ~03s
|+++++++++++++++++++++++++++++ | 57% ~03s
|++++++++++++++++++++++++++++++ | 59% ~03s
|++++++++++++++++++++++++++++++ | 60% ~03s
|+++++++++++++++++++++++++++++++ | 61% ~03s
|++++++++++++++++++++++++++++++++ | 62% ~03s
|++++++++++++++++++++++++++++++++ | 63% ~03s
|+++++++++++++++++++++++++++++++++ | 64% ~03s
|+++++++++++++++++++++++++++++++++ | 66% ~03s
|++++++++++++++++++++++++++++++++++ | 67% ~02s
|++++++++++++++++++++++++++++++++++ | 68% ~02s
|+++++++++++++++++++++++++++++++++++ | 69% ~02s
|++++++++++++++++++++++++++++++++++++ | 70% ~02s
|++++++++++++++++++++++++++++++++++++ | 71% ~02s
|+++++++++++++++++++++++++++++++++++++ | 72% ~02s
|+++++++++++++++++++++++++++++++++++++ | 74% ~02s
|++++++++++++++++++++++++++++++++++++++ | 75% ~02s
|++++++++++++++++++++++++++++++++++++++ | 76% ~02s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~02s
|++++++++++++++++++++++++++++++++++++++++ | 78% ~02s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~02s
|+++++++++++++++++++++++++++++++++++++++++ | 80% ~01s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~01s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~01s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~01s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~01s
|++++++++++++++++++++++++++++++++++++++++++++ | 86% ~01s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=07s Calculating cluster 11
| | 0 % ~calculating
|+ | 1 % ~09s
|++ | 2 % ~09s
|++ | 3 % ~09s
|+++ | 5 % ~09s
|+++ | 6 % ~08s
|++++ | 7 % ~08s
|+++++ | 8 % ~08s
|+++++ | 9 % ~08s
|++++++ | 10% ~08s
|++++++ | 11% ~08s
|+++++++ | 13% ~08s
|+++++++ | 14% ~07s
|++++++++ | 15% ~07s
|+++++++++ | 16% ~07s
|+++++++++ | 17% ~07s
|++++++++++ | 18% ~07s
|++++++++++ | 20% ~07s
|+++++++++++ | 21% ~07s
|+++++++++++ | 22% ~07s
|++++++++++++ | 23% ~07s
|+++++++++++++ | 24% ~07s
|+++++++++++++ | 25% ~06s
|++++++++++++++ | 26% ~06s
|++++++++++++++ | 28% ~06s
|+++++++++++++++ | 29% ~06s
|+++++++++++++++ | 30% ~06s
|++++++++++++++++ | 31% ~06s
|+++++++++++++++++ | 32% ~06s
|+++++++++++++++++ | 33% ~06s
|++++++++++++++++++ | 34% ~06s
|++++++++++++++++++ | 36% ~06s
|+++++++++++++++++++ | 37% ~05s
|+++++++++++++++++++ | 38% ~05s
|++++++++++++++++++++ | 39% ~05s
|+++++++++++++++++++++ | 40% ~05s
|+++++++++++++++++++++ | 41% ~05s
|++++++++++++++++++++++ | 43% ~05s
|++++++++++++++++++++++ | 44% ~05s
|+++++++++++++++++++++++ | 45% ~05s
|+++++++++++++++++++++++ | 46% ~05s
|++++++++++++++++++++++++ | 47% ~05s
|+++++++++++++++++++++++++ | 48% ~05s
|+++++++++++++++++++++++++ | 49% ~05s
|++++++++++++++++++++++++++ | 51% ~04s
|++++++++++++++++++++++++++ | 52% ~04s
|+++++++++++++++++++++++++++ | 53% ~04s
|++++++++++++++++++++++++++++ | 54% ~04s
|++++++++++++++++++++++++++++ | 55% ~04s
|+++++++++++++++++++++++++++++ | 56% ~04s
|+++++++++++++++++++++++++++++ | 57% ~04s
|++++++++++++++++++++++++++++++ | 59% ~04s
|++++++++++++++++++++++++++++++ | 60% ~04s
|+++++++++++++++++++++++++++++++ | 61% ~03s
|++++++++++++++++++++++++++++++++ | 62% ~03s
|++++++++++++++++++++++++++++++++ | 63% ~03s
|+++++++++++++++++++++++++++++++++ | 64% ~03s
|+++++++++++++++++++++++++++++++++ | 66% ~03s
|++++++++++++++++++++++++++++++++++ | 67% ~03s
|++++++++++++++++++++++++++++++++++ | 68% ~03s
|+++++++++++++++++++++++++++++++++++ | 69% ~03s
|++++++++++++++++++++++++++++++++++++ | 70% ~03s
|++++++++++++++++++++++++++++++++++++ | 71% ~03s
|+++++++++++++++++++++++++++++++++++++ | 72% ~02s
|+++++++++++++++++++++++++++++++++++++ | 74% ~02s
|++++++++++++++++++++++++++++++++++++++ | 75% ~02s
|++++++++++++++++++++++++++++++++++++++ | 76% ~02s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~02s
|++++++++++++++++++++++++++++++++++++++++ | 78% ~02s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~02s
|+++++++++++++++++++++++++++++++++++++++++ | 80% ~02s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~02s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~02s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~01s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~01s
|++++++++++++++++++++++++++++++++++++++++++++ | 86% ~01s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=09s top5 <- ClusterMarkers %>% group_by(cluster) %>% top_n(n=5, wt = avg_log2FC)
DoHeatmap(seu.q, features = top5$gene, size=3, angle =90, group.bar.height = 0.02, group.by = 'RNA_snn_res.0.6')Warning in DoHeatmap(seu.q, features = top5$gene, size = 3, angle = 90, :
The following features were omitted as they were not found in the scale.data slot for the RNA assay: MTRNR2L8, MTRNR2L12, SLC1A3, TPT1, RPL37
write.csv(ClusterMarkers,"/Users/rhalenathomas/Documents/Data/scRNAseq/PhenoID/scRNAseqSorted/Neurons1ClusterMarkers11.csv")
Cluster 0 has fewer markers. 2 and 5 have similar up reg markers 3 and 4 also overlap
Look at the cluster markers in cell type libraries for Neurons 2
N1.c6 <- ClusterMarkers %>% filter(cluster == 11 & avg_log2FC > 0)
genes <- N1.c6$gene
N1.c6.Er <- enrichr(genes, databases = db)
Uploading data to Enrichr... Done.
Querying Allen_Brain_Atlas_up... Done.
Querying Descartes_Cell_Types_and_Tissue_2021... Done.
Querying CellMarker_Augmented_2021... Done.
Querying Azimuth_Cell_Types_2021... Done.
Parsing results... Done.plotEnrich(N1.c6.Er[[1]], showTerms = 20, numChar = 40, y = "Count", orderBy = "P.value")
plotEnrich(N1.c6.Er[[2]], showTerms = 20, numChar = 40, y = "Count", orderBy = "P.value")
plotEnrich(N1.c6.Er[[3]], showTerms = 20, numChar = 40, y = "Count", orderBy = "P.value")
plotEnrich(N1.c6.Er[[4]], showTerms = 20, numChar = 40, y = "Count", orderBy = "P.value")
N1.Er.genes.1 <- N1.c6.Er[[1]] %>% select(Term, Genes, Combined.Score)
N1.Er.genes.1
N1.Er.genes.2 <- N1.c6.Er[[2]] %>% select(Term, Genes, Combined.Score)
N1.Er.genes.2
N1.Er.genes.3 <- N1.c6.Er[[3]] %>% select(Term, Genes, Combined.Score)
N1.Er.genes.3
N1.Er.genes.4 <- N1.c6.Er[[4]] %>% select(Term, Genes, Combined.Score)
N1.Er.genes.4NAcluster 0 - astrocyte, radial glia, microglia, striatum - very few genes in these terms cluster 1 - adrenal, thalmus, endothelial, astrocytes, neurons clusters 2 - neural plate, stratum, neurons, NPC, stem, astro,neuroendocrine cluster 3 - brain molecular layer, endothelial, astrocyte embryonic, cluster 4 - DG, striatum CA3, GABAergic neurons cluster 5 - DG, neurons, Glutamatergic neurons cluster 6 - neurons, astrocyte, microglia, GABAneurons cluster 7 - neurons, glut and gaba cluster 8 - astrocyte, endothelial, pericyte cluster 9 - epithelial, embryonic astrocytes, GABA neurons cluster 10 - epithelial cluster 11 - endothelial, immune cells T cells
Expression of markers genes in Neurons2
features <- c("PTPRC","AIF1","ADGRE1", "VIM", "TNC","PTPRZ1","FAM107A","HOPX","LIFR",
"ITGB5","IL6ST")
DoHeatmap(seu.q, features = features, size=3, angle =90, group.bar.height = 0.02, group.by = 'RNA_snn_res.0.6')
Warning in DoHeatmap(seu.q, features = features, size = 3, angle = 90, group.bar.height = 0.02, :
The following features were omitted as they were not found in the scale.data slot for the RNA assay: IL6ST, ITGB5, LIFR, HOPX, FAM107A, ADGRE1
DotPlot(seu.q, features = features, group.by = 'RNA_snn_res.0.6')+RotatedAxis()
Label the Neuron2 FACS population
Idents(seu.q) <- 'RNA_snn_res.0.6'
cluster.ids <- c("Neurons1","Neurons2","Neurons3",
"Other","DAneurons1","DAneurons2","Neurons4",
"DAneurons3","RG","Neurons2","Epithelial","Endothelial")
unique(seu.q$RNA_snn_res.0.6)
[1] 0 2 1 6 8 3 5 7 4 10 9 11
Levels: 0 1 2 3 4 5 6 7 8 9 10 11names(cluster.ids) <- levels(seu.q)
seu.q <- RenameIdents(seu.q, cluster.ids)
seu.q$number.groups <- Idents(seu.q)
DimPlot(seu.q, reduction = "umap", label = TRUE, group.by = 'number.groups', repel = TRUE)
Proportions of cell types
Find markers in pairs to go back and classify the subgroups. Will need to return to this for Neurons1 FACS
Neurons 5 and Neurons2 had similar markers and were merged Subset again
top5 <- neuron.sub.markers %>% group_by(cluster) %>% top_n(n=5, wt = avg_log2FC)
DoHeatmap(neuron.sub, features = top5$gene, size=3, angle =90, group.bar.height = 0.02)
DotPlot(neuron.sub, features = top5$gene) + RotatedAxis()
Warning: Scaling data with a low number of groups may produce misleading results
Use subgrouping and find cluster markers to look at neuronal subtypes.
top5 <- neuron.sub.markers %>% group_by(cluster) %>% top_n(n=5, wt = avg_log2FC)
DoHeatmap(neuron.sub, features = top5$gene, size=3, angle =90, group.bar.height = 0.02)
DotPlot(neuron.sub, features = top5$gene) + RotatedAxis()
Warning: Scaling data with a low number of groups may produce misleading results
Name the Neurons2 FACS population with the Neuron subtype latter.
Idents(seu.q) <- 'RNA_snn_res.0.6'
cluster.ids <- c("Neurons-MSX1","Neurons-LY6H","Neurons-ASCL1",
"Other","DAneurons-CYP1B1","DAneurons-ASCL1","Neurons-GK5",
"DAneurons-NEUROD1","RG","Neurons-LY6H","Epithelial","Endothelial")
unique(seu.q$RNA_snn_res.0.6) [1] 0 2 1 6 8 3 5 7 4 10 9 11
Levels: 0 1 2 3 4 5 6 7 8 9 10 11names(cluster.ids) <- levels(seu.q)
seu.q <- RenameIdents(seu.q, cluster.ids)
seu.q$cellsubgroups <- Idents(seu.q)
DimPlot(seu.q, reduction = "umap", label = TRUE, group.by = 'cellsubgroups', repel = TRUE)
NA
NA
# save the Neurons2 with labels
saveRDS(seu.q, "/Users/rhalenathomas/Documents/Data/scRNAseq/PhenoID/scRNAseqSorted/objs/Neurons2LabelsSeu30092022.RDS")
FACS population Glia1 (should be astrocytes)
seu.ft
An object of class Seurat
33541 features across 47295 samples within 2 assays
Active assay: RNA (33538 features, 0 variable features)
1 other assay present: HTO
saveRDS(seu.ft, "/Users/rhalenathomas/Documents/Data/scRNAseq/PhenoID/scRNAseqSorted/objs/Glia1AstroSeu01102022.RDS")Doublet finder
suppressMessages(require(DoubletFinder))
# filtering out MALAT1 and mitochondrial genes
seu.ft <- seu.ft[!grepl("MALAT1", rownames(seu.ft)), ]
seu.ft <- seu.ft[!grepl("^MT-", rownames(seu.ft)), ]
# like in the tutorial I'm following MALAT1 is the top most expressed gene. The top genes are a lot of MT and Ribosomal genes
seu.ft[["percent.rb"]] <- PercentageFeatureSet(seu.ft, pattern = "^RP")
# down sample there are too many cells to run doublet finder
seu.sub <- subset(seu.ft, downsample = 20000)
seu.d = NormalizeData(seu.sub)Performing log-normalization
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|seu.d = FindVariableFeatures(seu.d, verbose = F)
seu.d = ScaleData(seu.d, vars.to.regress = c("nFeature_RNA", "percent.mt"),
verbose = F)
seu.d = RunPCA(seu.d, verbose = F, npcs = 15)
seu.d = RunUMAP(seu.d, dims = 1:10, verbose = F)
nExp <- round(ncol(seu.d) * 0.15) # expect more doublets because there is a lot more cells
seu.d <- doubletFinder_v3(seu.d, pN = 0.25, pK = 0.09, nExp = nExp, PCs = 1:10)Loading required package: fields
Loading required package: spam
Spam version 2.9-1 (2022-08-07) is loaded.
Type 'help( Spam)' or 'demo( spam)' for a short introduction
and overview of this package.
Help for individual functions is also obtained by adding the
suffix '.spam' to the function name, e.g. 'help( chol.spam)'.
Attaching package: ‘spam’
The following object is masked from ‘package:Matrix’:
det
The following objects are masked from ‘package:base’:
backsolve, forwardsolve
Loading required package: viridis
Loading required package: viridisLite
Try help(fields) to get started.
Loading required package: KernSmooth
KernSmooth 2.23 loaded
Copyright M. P. Wand 1997-2009[1] "Creating 6667 artificial doublets..."
[1] "Creating Seurat object..."
[1] "Normalizing Seurat object..."Performing log-normalization
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|[1] "Finding variable genes..."Calculating gene variances
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating feature variances of standardized and clipped values
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|[1] "Scaling data..."Centering and scaling data matrix
|
| | 0%
|
|==================================== | 50%
|
|=========================================================================| 100%[1] "Running PCA..."
[1] "Calculating PC distance matrix..."
[1] "Computing pANN..."
[1] "Classifying doublets.."# the memory limit is reached here - I could run on compute canada
# For now I'll downsample
# this works
# name of the DF prediction can change, so extract the correct column name.
DF.name = colnames(seu.d@meta.data)[grepl("DF.classification", colnames(seu.d@meta.data))]
cowplot::plot_grid(ncol = 2, DimPlot(seu.d, group.by = "orig.ident") + NoAxes(),
DimPlot(seu.d, group.by = DF.name) + NoAxes())
VlnPlot(seu.d, features = "nFeature_RNA", group.by = DF.name, pt.size = 0.1)
Remove the doublet cells
dim(seu.d)
[1] 33524 17000dim(seu.sub)[1] 33524 20000Repeat workflow with doublet removed data and find clusters for
seu <- NormalizeData(seu.d, normalization.method = "LogNormalize", scale.factor = 10000)Performing log-normalization
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|seu <- FindVariableFeatures(seu, selection.method = "vst", nfeatures = 2000)Calculating gene variances
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating feature variances of standardized and clipped values
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|seu <- ScaleData(seu)Centering and scaling data matrix
|
| | 0%
|
|==================================== | 50%
|
|=========================================================================| 100%seu <- RunPCA(seu)PC_ 1
Positive: TMSB10, COL3A1, LGALS1, IGFBP4, PPIB, DCN, COL1A1, CALD1, S100A6, COL1A2
TIMP1, COL6A3, S100A4, NDUFA4L2, LUM, IFITM3, VIM, S100A11, VCAN, COL6A1
SELENOM, RPL10, RPL18A, APOE, RACK1, MDK, LGALS3, COL6A2, SERPINF1, RPS23
Negative: PMCH, CLU, AF131216.1, YJEFN3, AC068389.1, AC005014.2, MMP21, PTPRZ1, PLEK2, AC096667.1
S100A9, PITX1, AC010524.1, INA, DDI1, GPHA2, AC240565.1, IMPG2, AC243829.1, AC098934.4
ADAMTS20, DMRT3, AC022784.1, AC034198.2, MFNG, STPG3-AS1, PTPRH, AC092634.5, IGKV1-6, AC010422.2
PC_ 2
Positive: COL3A1, COL1A2, COL1A1, DCN, VCAN, LUM, COL6A3, IGFBP4, S100A4, COL6A1
NDUFA4L2, APOD, COL4A1, OGN, COL6A2, APOE, S100A6, MFAP4, COL4A2, EDNRA
PRRX1, TMSB10, COL5A2, TNFAIP6, THY1, FN1, COL5A1, IFITM3, CYTL1, CFH
Negative: S100B, IGFBP2, TPBG, CRYAB, LY6H, HPD, KRT18, GSTP1, KRT8, METRN
SOX2, ARL4C, ITM2C, PTPRZ1, SERTM1, HES1, DPCD, CA2, PLTP, ABHD14A
PAPPA2, MGST1, CBLN1, PSAT1, BEX1, ESM1, WFIKKN2, HIST1H4C, EFEMP1, LIX1
PC_ 3
Positive: PTGDS, SERPINF1, ECEL1, PRSS56, APOC1, BST2, ID1, FABP5, CITED4, RBP1
ASS1, TMEM176A, CDKN1C, TMEM176B, KDR, DCN, EMX2, ASCL1, APOE, SOD3
CCNG2, RPL39, DES, EMX2OS, WIF1, ADRA2C, RPL10, RPL7A, RPL32, RAMP1
Negative: SCG2, PTPRZ1, ESM1, COL1A1, COL1A2, TFPI2, GABBR2, VCAN, FN1, OGN
ABCA8, GNG11, LUM, NTRK2, COL3A1, S100A10, SOX2, RFX4, COL4A1, COL6A1
PTN, ITPR2, CBLN1, APOD, SPARCL1, NR2F1, LY6H, TNFAIP6, SLC7A11, ANXA1
PC_ 4
Positive: CXCL14, IGFBP2, PCP4, OGN, CA2, CP, WFIKKN2, IGFBP7, IGF1, PLAC9
SPINT2, FHIT, GPNMB, KRT18, EFEMP1, DKK3, COL1A2, KRT8, FOLR1, VCAN
DMKN, CRABP1, TPBG, SERF2, ENPP2, COL1A1, PRRX1, CRABP2, TNFAIP6, KCNJ13
Negative: APOE, PTN, DLK1, SCG2, APOC1, DCN, FABP5, GDF10, PTPRZ1, CLU
ESM1, KDR, PRSS56, PEG10, TFPI2, SPARCL1, RDH10, RFX4, GNG11, SLC7A2
IFITM1, BST2, GABBR2, ABCA8, COL23A1, ITM2C, CITED4, KCNQ1OT1, CCNG2, SYNDIG1
PC_ 5
Positive: KCNQ1OT1, NEAT1, DSP, CYP1B1, CP, KHDRBS2, SLC5A3, SLC4A10, AKAP12, LINC00473
PTP4A1, HMGCS1, PAPPA2, IGFBP3, WFIKKN2, AC092683.1, HTR2C, IGFBP5, WIF1, SLCO1C1
UACA, TRPM3, LAMB1, KCNJ13, SPARCL1, MDM2, RAB3IP, KDR, CA2, SV2C
Negative: RPS12, RPL10, RPL39, RPS23, RPL32, FTL, RPS28, RPL26, RPL12, RPS3
RPL7A, RPL8, RPS4X, RPL18A, GSTP1, IFI27L2, VIM, FTH1, S100A10, RACK1
IFI27, GAPDH, SERF2, GNG11, TXN, LY6H, HIST1H4C, LGALS1, TFPI2, RPS4Y1 seu <- RunUMAP(seu, reduction = "pca", n.neighbors = 43, dims = 1:30)12:20:48 UMAP embedding parameters a = 0.9922 b = 1.112
12:20:48 Read 20000 rows and found 30 numeric columns
12:20:48 Using Annoy for neighbor search, n_neighbors = 43
12:20:48 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
12:20:49 Writing NN index file to temp file /var/folders/k4/khtkczkd5tn732ftjpwgtr240000gn/T//RtmpOCo8CT/file74b220cfcf06
12:20:49 Searching Annoy index using 1 thread, search_k = 4300
12:20:55 Annoy recall = 100%
12:20:56 Commencing smooth kNN distance calibration using 1 thread with target n_neighbors = 43
12:20:57 Initializing from normalized Laplacian + noise (using irlba)
12:20:58 Commencing optimization for 200 epochs, with 1288572 positive edges
Using method 'umap'
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
12:21:12 Optimization finishedDimPlot(seu, reduction = "umap")
seu.q <- FindNeighbors(seu, dims = 1:25, k.param = 43)Computing nearest neighbor graph
Computing SNNseu.q <- FindClusters(seu.q, resolution = c(0,0.2,0.4,0.6))Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 20000
Number of edges: 760620
Running Louvain algorithm...0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|Maximum modularity in 10 random starts: 1.0000
Number of communities: 1
Elapsed time: 3 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 20000
Number of edges: 760620
Running Louvain algorithm...0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|Maximum modularity in 10 random starts: 0.8636
Number of communities: 6
Elapsed time: 2 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 20000
Number of edges: 760620
Running Louvain algorithm...0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|Maximum modularity in 10 random starts: 0.8123
Number of communities: 10
Elapsed time: 3 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 20000
Number of edges: 760620
Running Louvain algorithm...0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|Maximum modularity in 10 random starts: 0.7734
Number of communities: 11
Elapsed time: 4 secondsseu.q <- FindClusters(seu.q, resolution = c(0,0.05,0.1,0.8))Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 20000
Number of edges: 760620
Running Louvain algorithm...0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|Maximum modularity in 10 random starts: 1.0000
Number of communities: 1
Elapsed time: 3 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 20000
Number of edges: 760620
Running Louvain algorithm...0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|Maximum modularity in 10 random starts: 0.9522
Number of communities: 3
Elapsed time: 3 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 20000
Number of edges: 760620
Running Louvain algorithm...0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|Maximum modularity in 10 random starts: 0.9166
Number of communities: 4
Elapsed time: 3 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 20000
Number of edges: 760620
Running Louvain algorithm...0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|Maximum modularity in 10 random starts: 0.7376
Number of communities: 13
Elapsed time: 4 secondslibrary(clustree)Loading required package: ggraph
Attaching package: ‘ggraph’
The following object is masked from ‘package:sp’:
geometryclustree(seu.q)
DimPlot(seu.q, reduction = "umap", group.by = 'RNA_snn_res.0.05')
DimPlot(seu.q, reduction = "umap", group.by = 'RNA_snn_res.0.1')
DimPlot(seu.q, reduction = "umap", group.by = 'RNA_snn_res.0.2')
DimPlot(seu.q, reduction = "umap", group.by = 'RNA_snn_res.0.4')
DimPlot(seu.q, reduction = "umap", group.by = 'RNA_snn_res.0.6')
DimPlot(seu.q, reduction = "umap", group.by = 'RNA_snn_res.0.8')
NA
NA
NA
NALook at some expression markers in a feature plot
# genes reported up in Astrocytes
FeaturePlot(seu.q, features = c("GFAP","S100B","AQP4","SLC1A3","GJA1",
"APOE","TEAD1","GSTA4","SOX9",
"VIM","HMG20A","ALDH1L1"))
# almost no GFAP expression and lots of S100B everywhere
Predict cell types
# SNCA and control midbrain organoids 165 days in culture
MBO <- readRDS("/Users/rhalenathomas/Documents/Data/scRNAseq/AST23_BrainComm/MBOclusters_names29072021.rds")
# Midbrain AIW002 120 days in culture
AIWMBO <- readRDS("/Users/rhalenathomas/Documents/Data/scRNAseq/AIWtrio120days/MOintegratedClusterK123res0.8.names_nov16_2021")
# Midbrain AIW002 60 days in culture
AIW60 <- readRDS("/Users/rhalenathomas/Documents/Data/scRNAseq/AIWtrio60days/AWI002ParkinKOPinkKO60days_labels_14052022.rds")
#first predict with the MBO data
Idents(MBO) <- "cluster_labels"
DefaultAssay(MBO) <- "RNA"
# find the reference anchors
print("finding reference anchors")[1] "finding reference anchors"anchors <- FindTransferAnchors(reference = MBO ,query = seu.q, dims = 1:25)Performing PCA on the provided reference using 1997 features as input.
Projecting cell embeddings
Finding neighborhoods
Finding anchors
Found 814 anchors
Filtering anchors
Retained 219 anchors
as(<ngCMatrix>, "dgCMatrix") is deprecated since Matrix 1.5-0; do as(., "dMatrix") insteadprint("getting predictions")[1] "getting predictions"predictions <- TransferData(anchorset = anchors, refdata = MBO$cluster_labels)Finding integration vectors
Finding integration vector weights
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Predicting cell labelsseu.q <- AddMetaData(seu.q, metadata = predictions)
print(table(seu.q$predicted.id))
Epithelial Neurons-e Oligodendrocytes RGd1
587 451 11247 7689
RGd2
26 Idents(seu.q) <- 'predicted.id'
# add new dataslot for MBO predicted ID to make the next prediction
seu.q$MBOAST23.pred <- Idents(seu.q)
DimPlot(seu.q, group.by = 'MBOAST23.pred', label = TRUE)
## check the proportion of cell types predicted in each cluster
t.lables <- as.data.frame(table(seu.q$RNA_snn_res.0.8, seu.q$MBOAST23.pred))
t.lables$Freq <- as.double(t.lables$Freq)
# try bar chart
ggplot(t.lables, aes(y = Freq, x = Var1, fill = Var2)) + geom_bar(position = "stack", stat= "identity")
# clusters don't break up by the predicted cell types
############ another predictions now using the AIW organoids
Idents(AIWMBO) <- "res08names"
DefaultAssay(AIWMBO) <- "RNA"
anchors <- FindTransferAnchors(reference = AIWMBO ,query = seu.q, dims = 1:25)Performing PCA on the provided reference using 2000 features as input.
Projecting cell embeddings
Finding neighborhoods
Finding anchors
Found 3639 anchors
Filtering anchors
Retained 1713 anchorsprint("getting predictions")[1] "getting predictions"predictions <- TransferData(anchorset = anchors, refdata = AIWMBO$res08names)Finding integration vectors
Finding integration vector weights
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Predicting cell labelsseu.q <- AddMetaData(seu.q, metadata = predictions)
print(table(seu.q$predicted.id))
epithelial Neurons_DA Neurons_early_inh oligo
60 1 5622 110
OPC RGa RGd1 RGd3
241 13950 3 13 Idents(seu.q) <- 'predicted.id'
# add new dataslot for MBO predicted ID to make the next prediction
seu.q$MBOAIW.pred <- Idents(seu.q)
DimPlot(seu.q, group.by = 'MBOAIW.pred', label = TRUE)
## check the proportion of cell types predicted in each cluster
t.lables <- as.data.frame(table(seu.q$RNA_snn_res.0.8, seu.q$MBOAIW.pred))
t.lables$Freq <- as.double(t.lables$Freq)
# try bar chart
ggplot(t.lables, aes(y = Freq, x = Var1, fill = Var2)) + geom_bar(position = "stack", stat= "identity")
# the predicted cell types make more sense from the AIW002 organoid
# now predict with the AIW002 60 days organoid
Idents(AIW60) <- "cluster.ids"
DefaultAssay(AIW60) <- "RNA"
anchors <- FindTransferAnchors(reference = AIW60, query = seu.q, dims = 1:25)Performing PCA on the provided reference using 1999 features as input.
Projecting cell embeddings
Finding neighborhoods
Finding anchors
Found 2402 anchors
Filtering anchors
Retained 645 anchorsprint("getting predictions")[1] "getting predictions"predictions <- TransferData(anchorset = anchors, refdata = AIW60$cluster.ids) Finding integration vectors
Finding integration vector weights
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Predicting cell labelsseu.q <- AddMetaData(seu.q, metadata = predictions)
print(table(seu.q$predicted.id))
Astrocytes Dopaminergic Neurons early 1
1 31
Neural Epithelial Neural Precursors
12 7145
Radial Glia 1 Radial Glia 2
12806 5 Idents(seu.q) <- 'predicted.id'
# add new dataslot for MBO predicted ID to make the next prediction
seu.q$AIW60.pred <- Idents(seu.q)
DimPlot(seu.q, group.by = 'AIW60.pred', label = TRUE)
## check the proportion of cell types predicted in each cluster
t.lables <- as.data.frame(table(seu.q$RNA_snn_res.0.8, seu.q$AIW60.pred))
t.lables$Freq <- as.double(t.lables$Freq)
# try bar chart
ggplot(t.lables, aes(y = Freq, x = Var1, fill = Var2)) + geom_bar(position = "stack", stat= "identity")
# save ojbect with predicitons
saveRDS(seu.q, "/Users/rhalenathomas/Documents/Data/scRNAseq/PhenoID/scRNAseqSorted/objs/Glia1PredictionsSeu01102022.RDS")
Look at the reference objects
# triplication
DimPlot(MBO)
# AIW 60 days
DimPlot(AIW60)
# AIW 120 days
DimPlot(AIWMBO)
NA
NALook more at the predictions
# AIW002 120 days predictions
t.lables <- as.data.frame(table(seu.q$RNA_snn_res.0.2, seu.q$MBOAIW.pred))
t.lables$Freq <- as.double(t.lables$Freq)
ggplot(t.lables, aes(y = Freq, x = Var1, fill = Var2)) + geom_bar(position = "stack", stat= "identity") + RotatedAxis() 
top.pred.celltype.AIW120 <- as.data.frame(t.lables %>% group_by(Var1) %>% top_n(2, Freq))
df.top.aiw120 <- top.pred.celltype.AIW120[order(top.pred.celltype.AIW120$Var1,-top.pred.celltype.AIW120$Freq),]
row.names(df.top.aiw120) <- NULL
df.top.aiw120$I <- row.names(df.top.aiw120)
# AIW002 60 days predictions
t.lables <- as.data.frame(table(seu.q$RNA_snn_res.0.2, seu.q$AIW60.pred))
t.lables$Freq <- as.double(t.lables$Freq)
ggplot(t.lables, aes(y = Freq, x = Var1, fill = Var2)) + geom_bar(position = "stack", stat= "identity") + RotatedAxis() 
top.pred.celltype.AIW60 <-as.data.frame(t.lables %>% group_by(Var1) %>% top_n(2, Freq))
df.top.aiw60 <- top.pred.celltype.AIW60[order(top.pred.celltype.AIW60$Var1,-top.pred.celltype.AIW60$Freq),]
row.names(df.top.aiw60) <- NULL
df.top.aiw60$I <- row.names(df.top.aiw60)
# AST23 165 days predictions
t.lables <- as.data.frame(table(seu.q$RNA_snn_res.0.2, seu.q$MBOAST23.pred))
t.lables$Freq <- as.double(t.lables$Freq)
ggplot(t.lables, aes(y = Freq, x = Var1, fill = Var2)) + geom_bar(position = "stack", stat= "identity") + RotatedAxis() 
top.pred.celltype.AST23 <- as.data.frame(t.lables %>% group_by(Var1) %>% top_n(2, Freq))
df.top.AST23 <- top.pred.celltype.AST23[order(top.pred.celltype.AST23$Var1,-top.pred.celltype.AST23$Freq),]
row.names(df.top.AST23) <- NULL
df.top.AST23$I <- row.names(df.top.AST23)
pred.table <- merge(df.top.AST23, df.top.aiw60, by = 'I', all = TRUE)
pred.table <- merge(pred.table, df.top.aiw120, by = 'I')
pred.tableNAThese predictions are not good. There are several astrocyte markers by expression levels. Everything is predicted as Radial glia or oligo dendrocytes
Try to predict with the astrocyte Kamath data
Look at cluster markers
top5 <- ClusterMarkers %>% group_by(cluster) %>% top_n(n=5, wt = avg_log2FC)
DoHeatmap(seu.q, features = top5$gene, size=3, angle =90, group.bar.height = 0.02)
Warning in DoHeatmap(seu.q, features = top5$gene, size = 3, angle = 90, :
The following features were omitted as they were not found in the scale.data slot for the RNA assay: LRRC75A, SNHG25
Check cell type markers with EnrichR
N1.c0 <- ClusterMarkers %>% filter(cluster == 5 & avg_log2FC > 0)
genes <- N1.c0$gene
N1.c0.Er <- enrichr(genes, databases = db)
Uploading data to Enrichr... Done.
Querying Allen_Brain_Atlas_up... Done.
Querying Descartes_Cell_Types_and_Tissue_2021... Done.
Querying CellMarker_Augmented_2021... Done.
Querying Azimuth_Cell_Types_2021... Done.
Parsing results... Done.plotEnrich(N1.c0.Er[[1]], showTerms = 20, numChar = 40, y = "Count", orderBy = "P.value")
plotEnrich(N1.c0.Er[[2]], showTerms = 20, numChar = 40, y = "Count", orderBy = "P.value")
plotEnrich(N1.c0.Er[[3]], showTerms = 20, numChar = 40, y = "Count", orderBy = "P.value")
plotEnrich(N1.c0.Er[[4]], showTerms = 20, numChar = 40, y = "Count", orderBy = "P.value")
N1.Er.genes.1 <- N1.c0.Er[[1]] %>% select(Term, Genes, Combined.Score)
N1.Er.genes.1
N1.Er.genes.2 <- N1.c0.Er[[2]] %>% select(Term, Genes, Combined.Score)
N1.Er.genes.2
N1.Er.genes.3 <- N1.c0.Er[[3]] %>% select(Term, Genes, Combined.Score)
N1.Er.genes.3
N1.Er.genes.4 <- N1.c0.Er[[4]] %>% select(Term, Genes, Combined.Score)
N1.Er.genes.4
# cluster 0 - Cell type marker library - Brain astrocyte top hit and embryonic astrocytes
# gene list in term: brain astrocyte EFEMP1;NFIA;LIX1;PSAP;KIF21A;S100B;CRYAB;DKK3
# embryo astrocytes SOX2;BEX1;HMGCS1;PTPRZ1;LIX1;S100B;DKK3;ITM2C
# cluster 1 - stem cell pericyte (brain), stelate, astrocyte
# cluster 2 - hypothalmus, endothelial cells, macrophage
# endothelial CSTB;PRELID1;MT1X;CRIP2;RHOC;TMEM141;MT2A;RPS28;CCDC85B;EIF3I;RBP1;ID1;C4ORF3;ID3;PCBD1;MSX1;PPIC
# cluster 3 - smooth muscle cells
# cluster 4 - NK cells, fibroblasts
# NK cells ITGB1;RAB5C;GSTP1;PDCD5;EEF1B2;TGOLN2;SDCBP;MT2A;LDHA;SNRPD2;YWHAQ;ZNF326;TMSB10;CCDC50
# fibroblasts COL3A1;CALD1;COL6A3
# cluster 5 - endothelial cells, NK cells, CD8+
# cluster 6 - stromal cells eurythroblasts, none-neuronal, oligo
# reran and now there are only 5 clusters
# repeat checking
Cluster 0 - astrocytes Cluster 1 - pericyte astrocyte (weak still) Cluster 2 - endothelial Cluster 3 - smooth muscle Cluster 4 - NK/fibroblast Cluster 5 - endothelial Cluster 6 - non- neuronal
VlnPlot(seu.q, features = c("CD44","ITGB1","S100B"), group.by = 'orig.ident')
VlnPlot(seu.ft, features = c("CD44","ITGB1","S100B"), group.by = 'orig.ident')
Check expression of known markers
features <- c("PTPRC","AIF1","ADGRE1", "VIM", "TNC","PTPRZ1","FAM107A","HOPX","LIFR",
"ITGB5","IL6ST")
DoHeatmap(seu.q, features = features, size=3, angle =90, group.bar.height = 0.02)
Warning in DoHeatmap(seu.q, features = features, size = 3, angle = 90, group.bar.height = 0.02) :
The following features were omitted as they were not found in the scale.data slot for the RNA assay: IL6ST, ITGB5, LIFR, ADGRE1, AIF1
DotPlot(seu.q, features = features)+RotatedAxis()
No TH expression
clusters 5 VIM highest, S100 B Cluster 4 Cluster 3 has some cells with high OTX2, NES indicates NPC/Precursors Cluster 2 has some SOX2 and PAX6 indicates NPC, VAMP2 indicating neurons, S100B highest and most - indicates astrocytes, also MBP indicates oligos Cluster 1 Cluster 0
Lable the clusters
Idents(seu.q) <- 'RNA_snn_res.0.2'
#seu.q <- BuildClusterTree(seu.q, reorder = TRUE, reorder.numeric = TRUE)
unique(seu.q$RNA_snn_res.0.2)[1] 0 1 4 3 2 5
Levels: 0 1 2 3 4 5cluster.ids <- c("Astrocytes1","Astrocytes2","Precursors","RG1","RG2","Endothelial")
names(cluster.ids) <- levels(seu.q)
seu.q <- RenameIdents(seu.q, cluster.ids)
seu.q$subgroups <- Idents(seu.q)
#DimPlot(seu.q, group.by = 'RNA_snn_res.0.2', label = TRUE)
DimPlot(seu.q, reduction = "umap", label = TRUE, group.by = 'subgroups', repel = TRUE)
# something weird is going on in the order
#saveRDS(seu.q, "/Users/rhalenathomas/Documents/Data/scRNAseq/PhenoID/scRNAseqSorted/objs/Glia1LabledSeu301102022.RDS")
Compare the Astrocyte groups and get some markers for sub groups
npc.markers <- npc.markers %>% filter(avg_log2FC > 0)
dim(npc.markers)
[1] 292 5Add subtype gene ids
Proportions of cells types
Quick check the Glial2
seu.ft <- subset(seu, subset = nFeature_RNA > 250 & nCount_RNA > 250 & nCount_RNA < 10000)
seu.ft
An object of class Seurat
33541 features across 7553 samples within 2 assays
Active assay: RNA (33538 features, 0 variable features)
1 other assay present: HTOVlnPlot(seu.ft, pt.size = 0.10, features = c("nFeature_RNA"), y.max = 2000)Warning: Removed 1310 rows containing non-finite values (stat_ydensity).
Warning: Removed 1310 rows containing missing values (geom_point).
VlnPlot(seu.ft.glia, features = c("CD44","S100B","ITGB1"))
VlnPlot(seu.ft, features = c("CD44","S100B","ITGB1"), group.by = 'orig.ident')
Levels seem similar in Glia1 and Glia2
Remove doublets and start to process Glia 2
suppressMessages(require(DoubletFinder))
# filtering out MALAT1 and mitochondrial genes
seu.ft <- seu.ft[!grepl("MALAT1", rownames(seu.ft)), ]
seu.ft <- seu.ft[!grepl("^MT-", rownames(seu.ft)), ]
# like in the tutorial I'm following MALAT1 is the top most expressed gene. The top genes are a lot of MT and Ribosomal genes
seu.ft[["percent.rb"]] <- PercentageFeatureSet(seu.ft, pattern = "^RP")
seu.d = NormalizeData(seu.ft)Performing log-normalization
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|seu.d = FindVariableFeatures(seu.d, verbose = F)
seu.d = ScaleData(seu.d, vars.to.regress = c("nFeature_RNA", "percent.mt"),
verbose = F)
seu.d = RunPCA(seu.d, verbose = F, npcs = 15)
seu.d = RunUMAP(seu.d, dims = 1:10, verbose = F)
nExp <- round(ncol(seu.d) * 0.08) # expect more doublets because there is a lot more cells
seu.d <- doubletFinder_v3(seu.d, pN = 0.25, pK = 0.09, nExp = nExp, PCs = 1:10)[1] "Creating 2518 artificial doublets..."
[1] "Creating Seurat object..."
[1] "Normalizing Seurat object..."Performing log-normalization
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|[1] "Finding variable genes..."Calculating gene variances
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating feature variances of standardized and clipped values
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|[1] "Scaling data..."Centering and scaling data matrix
|
| | 0%
|
|====================================== | 50%
|
|===========================================================================| 100%[1] "Running PCA..."
[1] "Calculating PC distance matrix..."
[1] "Computing pANN..."
[1] "Classifying doublets.."# the memory limit is reached here - I could run on compute canada
# For now I'll downsample
# this works
# name of the DF prediction can change, so extract the correct column name.
DF.name = colnames(seu.d@meta.data)[grepl("DF.classification", colnames(seu.d@meta.data))]
cowplot::plot_grid(ncol = 2, DimPlot(seu.d, group.by = "orig.ident") + NoAxes(),
DimPlot(seu.d, group.by = DF.name) + NoAxes())
VlnPlot(seu.d, features = "nFeature_RNA", group.by = DF.name, pt.size = 0.1)
seu.d <- seu.d[, seu.d@meta.data[, DF.name]== "Singlet"]
dim(seu.d)[1] 33524 6949dim(seu.ft)[1] 33524 7553Cluster
Predict cell types
#first predict with the MBO data
Idents(MBO) <- "cluster_labels"
DefaultAssay(MBO) <- "RNA"
# find the reference anchors
print("finding reference anchors")
[1] "finding reference anchors"anchors <- FindTransferAnchors(reference = MBO ,query = seu.q, dims = 1:25)Performing PCA on the provided reference using 1997 features as input.
Projecting cell embeddings
Finding neighborhoods
Finding anchors
Found 1209 anchors
Filtering anchors
Retained 559 anchorsprint("getting predictions")[1] "getting predictions"predictions <- TransferData(anchorset = anchors, refdata = MBO$cluster_labels)Finding integration vectors
Finding integration vector weights
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Predicting cell labelsseu.q <- AddMetaData(seu.q, metadata = predictions)
Idents(seu.q) <- 'predicted.id'
# add new dataslot for MBO predicted ID to make the next prediction
seu.q$MBOAST23.pred <- Idents(seu.q)
DimPlot(seu.q, group.by = 'MBOAST23.pred', label = TRUE)
# see how accurate the predictions are
seu.q$predicted.id <- ifelse(seu.q$prediction.score.max > 0.95, seu.q$predicted.id, "None")
Idents(seu.q) <- 'predicted.id'
seu.q$MBOAST23.thresh <- Idents(seu.q)
DimPlot(seu.q, group.by = 'predicted.id', label = TRUE)
table(seu.q$MBOAST23.pred)
RGd1 Oligodendrocytes Neurons-e Neural Precursors Neurons-DA
1176 4567 976 117 105
Epithelial
8 table(seu.q$MBOAST23.thresh)
RGd1 None Oligodendrocytes Neurons-e Neurons-DA
254 5203 1446 33 10
Neural Precursors
3
## check the proportion of cell types predicted in each cluster
t.lables <- as.data.frame(table(seu.q$RNA_snn_res.0.4, seu.q$MBOAST23.pred))
t.lables$Freq <- as.double(t.lables$Freq)
# try bar chart
ggplot(t.lables, aes(y = Freq, x = Var1, fill = Var2)) + geom_bar(position = "stack", stat= "identity")
# clusters don't break up by the predicted cell types
############ another predictions now using the AIW organoids
Idents(AIWMBO) <- "res08names"
DefaultAssay(AIWMBO) <- "RNA"
anchors <- FindTransferAnchors(reference = AIWMBO ,query = seu.q, dims = 1:25)Performing PCA on the provided reference using 2000 features as input.
Projecting cell embeddings
Finding neighborhoods
Finding anchors
Found 3816 anchors
Filtering anchors
Retained 2248 anchorsprint("getting predictions")[1] "getting predictions"predictions <- TransferData(anchorset = anchors, refdata = AIWMBO$res08names)Finding integration vectors
Finding integration vector weights
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Predicting cell labelsseu.q <- AddMetaData(seu.q, metadata = predictions)
print(table(seu.q$predicted.id))
epithelial Neurons_DA Neurons_early_inh oligo OPC
31 217 4407 76 119
RGa RGd3
2074 25 Idents(seu.q) <- 'predicted.id'
# add new dataslot for MBO predicted ID to make the next prediction
seu.q$AIW120.pred <- Idents(seu.q)
DimPlot(seu.q, group.by = 'AIW120.pred', label = TRUE)
## check the proportion of cell types predicted in each cluster
t.lables <- as.data.frame(table(seu.q$RNA_snn_res.0.4, seu.q$MBOAIW.pred))
t.lables$Freq <- as.double(t.lables$Freq)
# try bar chart
ggplot(t.lables, aes(y = Freq, x = Var1, fill = Var2)) + geom_bar(position = "stack", stat= "identity")
# see how accurate the predictions are
seu.q$predicted.id <- ifelse(seu.q$prediction.score.max > 0.95, seu.q$predicted.id, "None")
Idents(seu.q) <- 'predicted.id'
seu.q$AIW120.thresh <- Idents(seu.q)
DimPlot(seu.q, group.by = 'AIW120.thresh', label = TRUE)
table(seu.q$AIW120.pred)
RGa Neurons_early_inh Neurons_DA oligo OPC
2074 4407 217 76 119
RGd3 epithelial
25 31 table(seu.q$AIW120.thresh)
RGa Neurons_early_inh None Neurons_DA OPC
1052 2735 2861 177 110
RGd3 epithelial
10 4 # the predicted cell types make more sense from the AIW002 organoid
# now predict with the AIW002 60 days organoid
Idents(AIW60) <- "cluster.ids"
DefaultAssay(AIW60) <- "RNA"
anchors <- FindTransferAnchors(reference = AIW60, query = seu.q, dims = 1:25)Performing PCA on the provided reference using 1999 features as input.
Projecting cell embeddings
Finding neighborhoods
Finding anchors
Found 3345 anchors
Filtering anchors
Retained 1582 anchorsprint("getting predictions")[1] "getting predictions"predictions <- TransferData(anchorset = anchors, refdata = AIW60$cluster.ids) Finding integration vectors
Finding integration vector weights
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Predicting cell labelsseu.q <- AddMetaData(seu.q, metadata = predictions)
print(table(seu.q$predicted.id))
Dopaminergic Neurons early 1 Neural Epithelial Neural Precursors
218 25 4973
Radial Glia 1
1733 Idents(seu.q) <- 'predicted.id'
# add new dataslot for MBO predicted ID to make the next prediction
seu.q$AIW60.pred <- Idents(seu.q)
DimPlot(seu.q, group.by = 'AIW60.pred', label = TRUE)
## check the proportion of cell types predicted in each cluster
t.lables <- as.data.frame(table(seu.q$RNA_snn_res.0.4, seu.q$AIW60.pred))
t.lables$Freq <- as.double(t.lables$Freq)
# try bar chart
ggplot(t.lables, aes(y = Freq, x = Var1, fill = Var2)) + geom_bar(position = "stack", stat= "identity")
# see how accurate the predictions are
seu.q$predicted.id <- ifelse(seu.q$prediction.score.max > 0.95, seu.q$predicted.id, "None")
Idents(seu.q) <- 'predicted.id'
seu.q$AIW60.thresh <- Idents(seu.q)
DimPlot(seu.q, group.by = 'AIW60.thresh', label = TRUE)
table(seu.q$AIW60.pred)
Radial Glia 1 Neural Precursors Dopaminergic Neurons early 1
1733 4973 218
Neural Epithelial
25 table(seu.q$AIW60.thresh)
Radial Glia 1 Neural Precursors None
972 3138 2740
Dopaminergic Neurons early 1
99 # save with predictions so far
saveRDS(seu.q, "/Users/rhalenathomas/Documents/Data/scRNAseq/PhenoID/scRNAseqSorted/objs/Glia2LabledSeu03102022.RDS")
See how many cells are predicted as astrocytes with the threshold
DAsubtypes <- readRDS("/Users/rhalenathomas/Documents/Data/scRNAseq/Macosko_Data/DAsubgroups_processed.Rds")
Idents(astro.ref) <- "Cell_Subtype"
DefaultAssay(astro.ref) <- "RNA"
# find the reference anchors
print("finding reference anchors")
[1] "finding reference anchors"anchors <- FindTransferAnchors(reference = DAsubtypes ,query = seu.q, dims = 1:20)Error: vector memory exhausted (limit reached?)Do these get labelled as DA neurons too???
seu.q <- readRDS("/Users/rhalenathomas/Documents/Data/scRNAseq/PhenoID/scRNAseqSorted/objs/Glia2LabledSeu03102022.RDS")Warning message:
R graphics engine version 15 is not supported by this version of RStudio. The Plots tab will be disabled until a newer version of RStudio is installed. 
seu.q$predicted.id <- ifelse(seu.q$prediction.score.max > 0.95, seu.q$predicted.id, "none")
Idents(seu.q) <- 'predicted.id'
seu.q$da.pred.thresh <- Idents(seu.q)
DimPlot(seu.q, group.by = 'da.pred.thresh', label = TRUE)
table(seu.q$da.pred.thresh)
none CALB1_RBP4
6213 736 Compare predictions - make a predictions table
# AIW002 120 days predictions - take the thresholded options
t.lables <- as.data.frame(table(seu.q$RNA_snn_res.0.2, seu.q$AIW120.thresh))
t.lables$Freq <- as.double(t.lables$Freq)
ggplot(t.lables, aes(y = Freq, x = Var1, fill = Var2)) + geom_bar(position = "stack", stat= "identity") + RotatedAxis() 
top.pred.celltype.AIW120 <- as.data.frame(t.lables %>% group_by(Var1) %>% top_n(2, Freq))
df.top.aiw120 <- top.pred.celltype.AIW120[order(top.pred.celltype.AIW120$Var1,-top.pred.celltype.AIW120$Freq),]
row.names(df.top.aiw120) <- NULL
df.top.aiw120$I <- row.names(df.top.aiw120)
# AIW002 60 days predictions
t.lables <- as.data.frame(table(seu.q$RNA_snn_res.0.2, seu.q$AIW60.thresh))
t.lables$Freq <- as.double(t.lables$Freq)
ggplot(t.lables, aes(y = Freq, x = Var1, fill = Var2)) + geom_bar(position = "stack", stat= "identity") + RotatedAxis() *** recursive gc invocation
*** recursive gc invocation
*** recursive gc invocation
*** recursive gc invocation
*** recursive gc invocation
*** recursive gc invocation
*** recursive gc invocation
*** recursive gc invocation
top.pred.celltype.AIW60 <-as.data.frame(t.lables %>% group_by(Var1) %>% top_n(2, Freq))
df.top.aiw60 <- top.pred.celltype.AIW60[order(top.pred.celltype.AIW60$Var1,-top.pred.celltype.AIW60$Freq),]
row.names(df.top.aiw60) <- NULL
df.top.aiw60$I <- row.names(df.top.aiw60)
# AST23 165 days predictions
t.lables <- as.data.frame(table(seu.q$RNA_snn_res.0.2, seu.q$MBOAST23.thresh))
t.lables$Freq <- as.double(t.lables$Freq)
ggplot(t.lables, aes(y = Freq, x = Var1, fill = Var2)) + geom_bar(position = "stack", stat= "identity") + RotatedAxis() 
top.pred.celltype.AST23 <- as.data.frame(t.lables %>% group_by(Var1) %>% top_n(2, Freq))
df.top.AST23 <- top.pred.celltype.AST23[order(top.pred.celltype.AST23$Var1,-top.pred.celltype.AST23$Freq),]
row.names(df.top.AST23) <- NULL
df.top.AST23$I <- row.names(df.top.AST23)
# add the threshold Astro predictions
t.lables <- as.data.frame(table(seu.q$RNA_snn_res.0.2, seu.q$astro.pred.thresh))
t.lables$Freq <- as.double(t.lables$Freq)
ggplot(t.lables, aes(y = Freq, x = Var1, fill = Var2)) + geom_bar(position = "stack", stat= "identity") + RotatedAxis() 
top.pred.celltype.astro <- as.data.frame(t.lables %>% group_by(Var1) %>% top_n(2, Freq))
df.top.astro <- top.pred.celltype.astro[order(top.pred.celltype.astro$Var1,-top.pred.celltype.astro$Freq),]
row.names(df.top.astro) <- NULL
df.top.astro$I <- row.names(df.top.astro)
# add the neurons predictions
t.lables <- as.data.frame(table(seu.q$RNA_snn_res.0.2, seu.q$da.pred.thresh))
t.lables$Freq <- as.double(t.lables$Freq)
ggplot(t.lables, aes(y = Freq, x = Var1, fill = Var2)) + geom_bar(position = "stack", stat= "identity") + RotatedAxis() 
top.pred.celltype.da <- as.data.frame(t.lables %>% group_by(Var1) %>% top_n(2, Freq))
df.top.da <- top.pred.celltype.da[order(top.pred.celltype.da$Var1,-top.pred.celltype.da$Freq),]
row.names(df.top.da) <- NULL
df.top.da$I <- row.names(df.top.da)
pred.table <- merge(df.top.AST23, df.top.aiw60, by = 'I', all = TRUE)
pred.table <- merge(pred.table, df.top.aiw120, by = 'I')
pred.table <- merge(pred.table, df.top.astro, by = 'I')Warning in merge.data.frame(pred.table, df.top.astro, by = "I") :
column names ‘Var1.x’, ‘Var2.x’, ‘Freq.x’, ‘Var1.y’, ‘Var2.y’, ‘Freq.y’ are duplicated in the resultpred.table <- merge(pred.table, df.top.da, by = 'I')Warning in merge.data.frame(pred.table, df.top.da, by = "I") :
column names ‘Var1.x’, ‘Var2.x’, ‘Freq.x’, ‘Var1.y’, ‘Var2.y’, ‘Freq.y’ are duplicated in the resultpred.tableNA
NAPredicted cluster annotations 0 Unknown/ NPC 1 RG 2 astro 3 RG 4 neurons 5 RG
Look at gene lists with known markers
rg <- c("VIM","NES","PAX6","HES1","EAAT1","NCAD1","SOX2","FABP7")
DoHeatmap(seu.q, features = rg, size=3, angle =90, group.bar.height = 0.02)
Warning in DoHeatmap(seu.q, features = rg, size = 3, angle = 90, group.bar.height = 0.02) :
The following features were omitted as they were not found in the scale.data slot for the RNA assay: NCAD1, EAAT1, PAX6, NES
DotPlot(seu.q, features = rg)+RotatedAxis()Warning in FetchData.Seurat(object = object, vars = features, cells = cells) :
The following requested variables were not found: EAAT1, NCAD1
Marker expression predictions Cluster 0 - unknown Cluster 1 - RG Cluster 2 - unknown Cluster 3 - RG cluster 4 - immature neurons Cluster 5 - RG, opc
Check the levels of RNA in each cluster
VlnPlot(seu.q, features = "nFeature_RNA")
Cluster 0 and 2 have fewer sequences than other groups and thus no markers Possibly remove these is they don’t come up with some markers
Find cluster markers
Idents(seu.q) <- 'RNA_snn_res.0.1'
ClusterMarkers <- FindAllMarkers(seu.q, only.pos = TRUE)
Calculating cluster 0
| | 0 % ~calculating
|+ | 1 % ~10s
|++ | 2 % ~09s
|++ | 3 % ~09s
|+++ | 5 % ~09s
|+++ | 6 % ~09s
|++++ | 7 % ~09s
|+++++ | 8 % ~08s
|+++++ | 9 % ~08s
|++++++ | 10% ~08s
|++++++ | 12% ~08s
|+++++++ | 13% ~08s
|+++++++ | 14% ~08s
|++++++++ | 15% ~08s
|+++++++++ | 16% ~08s
|+++++++++ | 17% ~08s
|++++++++++ | 19% ~08s
|++++++++++ | 20% ~07s
|+++++++++++ | 21% ~08s
|++++++++++++ | 22% ~07s
|++++++++++++ | 23% ~07s
|+++++++++++++ | 24% ~07s
|+++++++++++++ | 26% ~07s
|++++++++++++++ | 27% ~07s
|++++++++++++++ | 28% ~07s
|+++++++++++++++ | 29% ~07s
|++++++++++++++++ | 30% ~07s
|++++++++++++++++ | 31% ~07s
|+++++++++++++++++ | 33% ~06s
|+++++++++++++++++ | 34% ~06s
|++++++++++++++++++ | 35% ~06s
|+++++++++++++++++++ | 36% ~06s
|+++++++++++++++++++ | 37% ~06s
|++++++++++++++++++++ | 38% ~06s
|++++++++++++++++++++ | 40% ~06s
|+++++++++++++++++++++ | 41% ~06s
|+++++++++++++++++++++ | 42% ~06s
|++++++++++++++++++++++ | 43% ~05s
|+++++++++++++++++++++++ | 44% ~05s
|+++++++++++++++++++++++ | 45% ~05s
|++++++++++++++++++++++++ | 47% ~05s
|++++++++++++++++++++++++ | 48% ~05s
|+++++++++++++++++++++++++ | 49% ~05s
|+++++++++++++++++++++++++ | 50% ~05s
|++++++++++++++++++++++++++ | 51% ~05s
|+++++++++++++++++++++++++++ | 52% ~04s
|+++++++++++++++++++++++++++ | 53% ~04s
|++++++++++++++++++++++++++++ | 55% ~04s
|++++++++++++++++++++++++++++ | 56% ~04s
|+++++++++++++++++++++++++++++ | 57% ~04s
|++++++++++++++++++++++++++++++ | 58% ~04s
|++++++++++++++++++++++++++++++ | 59% ~04s
|+++++++++++++++++++++++++++++++ | 60% ~04s
|+++++++++++++++++++++++++++++++ | 62% ~04s
|++++++++++++++++++++++++++++++++ | 63% ~03s
|++++++++++++++++++++++++++++++++ | 64% ~03s
|+++++++++++++++++++++++++++++++++ | 65% ~03s
|++++++++++++++++++++++++++++++++++ | 66% ~03s
|++++++++++++++++++++++++++++++++++ | 67% ~03s
|+++++++++++++++++++++++++++++++++++ | 69% ~03s
|+++++++++++++++++++++++++++++++++++ | 70% ~03s
|++++++++++++++++++++++++++++++++++++ | 71% ~03s
|+++++++++++++++++++++++++++++++++++++ | 72% ~03s
|+++++++++++++++++++++++++++++++++++++ | 73% ~02s
|++++++++++++++++++++++++++++++++++++++ | 74% ~02s
|++++++++++++++++++++++++++++++++++++++ | 76% ~02s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~02s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~02s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~02s
|+++++++++++++++++++++++++++++++++++++++++ | 80% ~02s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~02s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~02s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~01s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~01s
|++++++++++++++++++++++++++++++++++++++++++++ | 86% ~01s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++ | 88% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=09s Calculating cluster 1
| | 0 % ~calculating
|+ | 1 % ~23s
|++ | 2 % ~22s
|++ | 3 % ~21s
|+++ | 4 % ~22s
|+++ | 5 % ~21s
|++++ | 6 % ~21s
|++++ | 7 % ~21s
|+++++ | 8 % ~21s
|+++++ | 9 % ~21s
|++++++ | 10% ~20s
|++++++ | 11% ~20s
|+++++++ | 12% ~20s
|+++++++ | 13% ~19s
|++++++++ | 14% ~19s
|++++++++ | 15% ~19s
|+++++++++ | 16% ~19s
|+++++++++ | 18% ~18s
|++++++++++ | 19% ~18s
|++++++++++ | 20% ~18s
|+++++++++++ | 21% ~18s
|+++++++++++ | 22% ~17s
|++++++++++++ | 23% ~17s
|++++++++++++ | 24% ~17s
|+++++++++++++ | 25% ~17s
|+++++++++++++ | 26% ~16s
|++++++++++++++ | 27% ~16s
|++++++++++++++ | 28% ~16s
|+++++++++++++++ | 29% ~16s
|+++++++++++++++ | 30% ~15s
|++++++++++++++++ | 31% ~15s
|++++++++++++++++ | 32% ~15s
|+++++++++++++++++ | 33% ~15s
|++++++++++++++++++ | 34% ~14s
|++++++++++++++++++ | 35% ~14s
|+++++++++++++++++++ | 36% ~14s
|+++++++++++++++++++ | 37% ~14s
|++++++++++++++++++++ | 38% ~14s
|++++++++++++++++++++ | 39% ~13s
|+++++++++++++++++++++ | 40% ~13s
|+++++++++++++++++++++ | 41% ~13s
|++++++++++++++++++++++ | 42% ~13s
|++++++++++++++++++++++ | 43% ~12s
|+++++++++++++++++++++++ | 44% ~12s
|+++++++++++++++++++++++ | 45% ~12s
|++++++++++++++++++++++++ | 46% ~12s
|++++++++++++++++++++++++ | 47% ~12s
|+++++++++++++++++++++++++ | 48% ~11s
|+++++++++++++++++++++++++ | 49% ~11s
|++++++++++++++++++++++++++ | 51% ~11s
|++++++++++++++++++++++++++ | 52% ~11s
|+++++++++++++++++++++++++++ | 53% ~10s
|+++++++++++++++++++++++++++ | 54% ~10s
|++++++++++++++++++++++++++++ | 55% ~10s
|++++++++++++++++++++++++++++ | 56% ~10s
|+++++++++++++++++++++++++++++ | 57% ~09s
|+++++++++++++++++++++++++++++ | 58% ~09s
|++++++++++++++++++++++++++++++ | 59% ~09s
|++++++++++++++++++++++++++++++ | 60% ~09s
|+++++++++++++++++++++++++++++++ | 61% ~09s
|+++++++++++++++++++++++++++++++ | 62% ~08s
|++++++++++++++++++++++++++++++++ | 63% ~08s
|++++++++++++++++++++++++++++++++ | 64% ~08s
|+++++++++++++++++++++++++++++++++ | 65% ~08s
|+++++++++++++++++++++++++++++++++ | 66% ~07s
|++++++++++++++++++++++++++++++++++ | 67% ~07s
|+++++++++++++++++++++++++++++++++++ | 68% ~07s
|+++++++++++++++++++++++++++++++++++ | 69% ~07s
|++++++++++++++++++++++++++++++++++++ | 70% ~06s
|++++++++++++++++++++++++++++++++++++ | 71% ~06s
|+++++++++++++++++++++++++++++++++++++ | 72% ~06s
|+++++++++++++++++++++++++++++++++++++ | 73% ~06s
|++++++++++++++++++++++++++++++++++++++ | 74% ~06s
|++++++++++++++++++++++++++++++++++++++ | 75% ~05s
|+++++++++++++++++++++++++++++++++++++++ | 76% ~05s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~05s
|++++++++++++++++++++++++++++++++++++++++ | 78% ~05s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~04s
|+++++++++++++++++++++++++++++++++++++++++ | 80% ~04s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~04s
|++++++++++++++++++++++++++++++++++++++++++ | 82% ~04s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~04s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~03s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~03s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~03s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~03s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~02s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~02s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~02s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~02s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~02s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=21s Calculating cluster 2
| | 0 % ~calculating
|+ | 1 % ~20s
|++ | 2 % ~20s
|++ | 3 % ~20s
|+++ | 4 % ~20s
|+++ | 5 % ~19s
|++++ | 6 % ~19s
|++++ | 7 % ~19s
|+++++ | 8 % ~19s
|+++++ | 9 % ~18s
|++++++ | 10% ~18s
|++++++ | 11% ~18s
|+++++++ | 12% ~18s
|+++++++ | 13% ~18s
|++++++++ | 14% ~18s
|++++++++ | 15% ~17s
|+++++++++ | 16% ~17s
|+++++++++ | 18% ~17s
|++++++++++ | 19% ~17s
|++++++++++ | 20% ~16s
|+++++++++++ | 21% ~16s
|+++++++++++ | 22% ~16s
|++++++++++++ | 23% ~16s
|++++++++++++ | 24% ~16s
|+++++++++++++ | 25% ~15s
|+++++++++++++ | 26% ~15s
|++++++++++++++ | 27% ~15s
|++++++++++++++ | 28% ~15s
|+++++++++++++++ | 29% ~14s
|+++++++++++++++ | 30% ~14s
|++++++++++++++++ | 31% ~14s
|++++++++++++++++ | 32% ~14s
|+++++++++++++++++ | 33% ~14s
|++++++++++++++++++ | 34% ~13s
|++++++++++++++++++ | 35% ~13s
|+++++++++++++++++++ | 36% ~13s
|+++++++++++++++++++ | 37% ~13s
|++++++++++++++++++++ | 38% ~13s
|++++++++++++++++++++ | 39% ~12s
|+++++++++++++++++++++ | 40% ~12s
|+++++++++++++++++++++ | 41% ~12s
|++++++++++++++++++++++ | 42% ~12s
|++++++++++++++++++++++ | 43% ~12s
|+++++++++++++++++++++++ | 44% ~11s
|+++++++++++++++++++++++ | 45% ~11s
|++++++++++++++++++++++++ | 46% ~11s
|++++++++++++++++++++++++ | 47% ~11s
|+++++++++++++++++++++++++ | 48% ~10s
|+++++++++++++++++++++++++ | 49% ~10s
|++++++++++++++++++++++++++ | 51% ~10s
|++++++++++++++++++++++++++ | 52% ~10s
|+++++++++++++++++++++++++++ | 53% ~10s
|+++++++++++++++++++++++++++ | 54% ~09s
|++++++++++++++++++++++++++++ | 55% ~09s
|++++++++++++++++++++++++++++ | 56% ~09s
|+++++++++++++++++++++++++++++ | 57% ~09s
|+++++++++++++++++++++++++++++ | 58% ~09s
|++++++++++++++++++++++++++++++ | 59% ~08s
|++++++++++++++++++++++++++++++ | 60% ~08s
|+++++++++++++++++++++++++++++++ | 61% ~08s
|+++++++++++++++++++++++++++++++ | 62% ~08s
|++++++++++++++++++++++++++++++++ | 63% ~08s
|++++++++++++++++++++++++++++++++ | 64% ~07s
|+++++++++++++++++++++++++++++++++ | 65% ~07s
|+++++++++++++++++++++++++++++++++ | 66% ~07s
|++++++++++++++++++++++++++++++++++ | 67% ~07s
|+++++++++++++++++++++++++++++++++++ | 68% ~07s
|+++++++++++++++++++++++++++++++++++ | 69% ~06s
|++++++++++++++++++++++++++++++++++++ | 70% ~06s
|++++++++++++++++++++++++++++++++++++ | 71% ~06s
|+++++++++++++++++++++++++++++++++++++ | 72% ~06s
|+++++++++++++++++++++++++++++++++++++ | 73% ~05s
|++++++++++++++++++++++++++++++++++++++ | 74% ~05s
|++++++++++++++++++++++++++++++++++++++ | 75% ~05s
|+++++++++++++++++++++++++++++++++++++++ | 76% ~05s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~05s
|++++++++++++++++++++++++++++++++++++++++ | 78% ~04s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~04s
|+++++++++++++++++++++++++++++++++++++++++ | 80% ~04s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~04s
|++++++++++++++++++++++++++++++++++++++++++ | 82% ~04s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~03s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~03s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~03s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~03s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~03s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~02s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~02s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~02s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~02s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=20s Calculating cluster 3
| | 0 % ~calculating
|+ | 1 % ~15s
|++ | 2 % ~15s
|++ | 3 % ~15s
|+++ | 4 % ~15s
|+++ | 5 % ~15s
|++++ | 6 % ~15s
|++++ | 7 % ~14s
|+++++ | 9 % ~14s
|+++++ | 10% ~14s
|++++++ | 11% ~14s
|++++++ | 12% ~14s
|+++++++ | 13% ~14s
|+++++++ | 14% ~14s
|++++++++ | 15% ~13s
|++++++++ | 16% ~13s
|+++++++++ | 17% ~13s
|++++++++++ | 18% ~13s
|++++++++++ | 19% ~13s
|+++++++++++ | 20% ~12s
|+++++++++++ | 21% ~12s
|++++++++++++ | 22% ~12s
|++++++++++++ | 23% ~12s
|+++++++++++++ | 24% ~12s
|+++++++++++++ | 26% ~11s
|++++++++++++++ | 27% ~11s
|++++++++++++++ | 28% ~11s
|+++++++++++++++ | 29% ~11s
|+++++++++++++++ | 30% ~11s
|++++++++++++++++ | 31% ~10s
|++++++++++++++++ | 32% ~10s
|+++++++++++++++++ | 33% ~10s
|++++++++++++++++++ | 34% ~10s
|++++++++++++++++++ | 35% ~10s
|+++++++++++++++++++ | 36% ~10s
|+++++++++++++++++++ | 37% ~09s
|++++++++++++++++++++ | 38% ~09s
|++++++++++++++++++++ | 39% ~09s
|+++++++++++++++++++++ | 40% ~09s
|+++++++++++++++++++++ | 41% ~09s
|++++++++++++++++++++++ | 43% ~09s
|++++++++++++++++++++++ | 44% ~08s
|+++++++++++++++++++++++ | 45% ~08s
|+++++++++++++++++++++++ | 46% ~08s
|++++++++++++++++++++++++ | 47% ~08s
|++++++++++++++++++++++++ | 48% ~08s
|+++++++++++++++++++++++++ | 49% ~08s
|+++++++++++++++++++++++++ | 50% ~07s
|++++++++++++++++++++++++++ | 51% ~07s
|+++++++++++++++++++++++++++ | 52% ~07s
|+++++++++++++++++++++++++++ | 53% ~07s
|++++++++++++++++++++++++++++ | 54% ~07s
|++++++++++++++++++++++++++++ | 55% ~07s
|+++++++++++++++++++++++++++++ | 56% ~06s
|+++++++++++++++++++++++++++++ | 57% ~06s
|++++++++++++++++++++++++++++++ | 59% ~06s
|++++++++++++++++++++++++++++++ | 60% ~06s
|+++++++++++++++++++++++++++++++ | 61% ~06s
|+++++++++++++++++++++++++++++++ | 62% ~06s
|++++++++++++++++++++++++++++++++ | 63% ~06s
|++++++++++++++++++++++++++++++++ | 64% ~05s
|+++++++++++++++++++++++++++++++++ | 65% ~05s
|+++++++++++++++++++++++++++++++++ | 66% ~05s
|++++++++++++++++++++++++++++++++++ | 67% ~05s
|+++++++++++++++++++++++++++++++++++ | 68% ~05s
|+++++++++++++++++++++++++++++++++++ | 69% ~05s
|++++++++++++++++++++++++++++++++++++ | 70% ~04s
|++++++++++++++++++++++++++++++++++++ | 71% ~04s
|+++++++++++++++++++++++++++++++++++++ | 72% ~04s
|+++++++++++++++++++++++++++++++++++++ | 73% ~04s
|++++++++++++++++++++++++++++++++++++++ | 74% ~04s
|++++++++++++++++++++++++++++++++++++++ | 76% ~04s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~03s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~03s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~03s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~03s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~03s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~03s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~03s
|+++++++++++++++++++++++++++++++++++++++++++ | 84% ~02s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~02s
|++++++++++++++++++++++++++++++++++++++++++++ | 86% ~02s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~02s
|+++++++++++++++++++++++++++++++++++++++++++++ | 88% ~02s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~02s
|++++++++++++++++++++++++++++++++++++++++++++++ | 90% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=15s top5 <- ClusterMarkers %>% group_by(cluster) %>% top_n(n=5, wt = avg_log2FC)
DoHeatmap(seu.q, features = top5$gene, size=3, angle =90, group.bar.height = 0.02)
Markers of 2 are matching with 5 possibly merge these together Cluster 0 markers don’t look up regulated but the list is long
Look at the libraries
N1.c0 <- ClusterMarkers %>% filter(cluster == 0 & avg_log2FC > 0)
genes <- N1.c0$gene
N1.c0.Er <- enrichr(genes, databases = db)
Uploading data to Enrichr... Done.
Querying Descartes_Cell_Types_and_Tissue_2021... Done.
Querying CellMarker_Augmented_2021... Done.
Querying Azimuth_Cell_Types_2021... Done.
Parsing results... Done.plotEnrich(N1.c0.Er[[1]], showTerms = 20, numChar = 40, y = "Count", orderBy = "P.value")
plotEnrich(N1.c0.Er[[2]], showTerms = 20, numChar = 40, y = "Count", orderBy = "P.value")
plotEnrich(N1.c0.Er[[3]], showTerms = 20, numChar = 40, y = "Count", orderBy = "P.value")
N1.Er.genes.1 <- N1.c0.Er[[1]] %>% select(Term, Genes, Combined.Score)
N1.Er.genes.1
N1.Er.genes.2 <- N1.c0.Er[[2]] %>% select(Term, Genes, Combined.Score)
N1.Er.genes.2
N1.Er.genes.3 <- N1.c0.Er[[3]] %>% select(Term, Genes, Combined.Score)
N1.Er.genes.3Add some cell type annotations
Idents(seu.q) <- 'RNA_snn_res.0.2'
cluster.ids <- c("Glia1","RG1","Glia2","RG2","NeuronsImmature","RG3")
names(cluster.ids) <- levels(seu.q)
seu.q <- RenameIdents(seu.q, cluster.ids)
seu.q$subgroups <- Idents(seu.q)
#DimPlot(seu.q, group.by = 'RNA_snn_res.0.2', label = TRUE)
DimPlot(seu.q, reduction = "umap", label = TRUE, group.by = 'subgroups', repel = TRUE)
NA
NA
NA# save file
saveRDS(seu.q, "/Users/rhalenathomas/Documents/Data/scRNAseq/PhenoID/scRNAseqSorted/objs/Glia2LabledSeu03102022.RDS")
Main cell groups
Idents(seu.q) <- 'RNA_snn_res.0.2'
cluster.ids <- c("RG","RG","RG","RG","NeuronsImmature","RG")
names(cluster.ids) <- levels(seu.q)
seu.q <- RenameIdents(seu.q, cluster.ids)
seu.q$Cell_Types <- Idents(seu.q)
#DimPlot(seu.q, group.by = 'RNA_snn_res.0.2', label = TRUE)
DimPlot(seu.q, reduction = "umap", label = TRUE, group.by = 'Cell_Types', repel = TRUE)
saveRDS(seu.q, "/Users/rhalenathomas/Documents/Data/scRNAseq/PhenoID/scRNAseqSorted/objs/Glia2LabledSeu03102022.RDS")
Proportions of cell types
I’ll calculate the proportions for each cell type and make a table or plot in the comparison workbook.
---
title: "R Notebook"
output: html_notebook
---
Single cell seq after sorting for PhenoID

sample1 = Neurons1
sample2 = Neurons2
sample3 = Glia1 - Astrocytes (CD44+)
sample4 = Glia2 - Radial Glia (CD44-)

In HPC I have run steps of scrnabox (custom pipeline in progress)
1. Cell Ranger for feature seq
2. Create Seurat Objects 
3. Apply minimum filtering and calculate percent mitochondria.

I have technical 3 replicates with hashtag labels at this point I haven't yet demultiplex the hashtags. The data here will be treated as one sample.  I sorted three separate samples and pooled them together. 


```{r}
# set up the environment

library(Seurat)
library(dplyr)
library(Matrix)
library(ggplot2)

#rm(list = ls())


```


Read in the seurat objects made in compute canada

```{r}

# this seems to never load I'll use step 3 output that has some filtering 
# nFeature_RNA > 180 and percent.mt < 25

pathway <- "/Users/rhalenathomas/Documents/Data/scRNAseq/PhenoID/scRNAseqSorted/objs/"

Neurons1 <- readRDS(paste(pathway,"seu1.rds",sep = ""))
Neurons2 <- readRDS(paste(pathway,"seu2.rds",sep = ""))
Glia1 <- readRDS(paste(pathway,"seu3.rds",sep = ""))
Glia2 <- readRDS(paste(pathway,"seu4.rds",sep = ""))

Neurons1
Neurons2
Glia1
Glia2


```


Have a look at the objects that already have some filtering


See the violin plots 

```{r}

VlnPlot(Neurons1, pt.size = 0.10, features = c("nFeature_RNA", "nCount_RNA", "percent.mt"), ncol = 3)

VlnPlot(Neurons1, pt.size = 0.10, features = c("nFeature_RNA"), y.max = 500)
VlnPlot(Neurons1, pt.size = 0.10, features = c("nCount_RNA"), y.max = 2000)



# filter more cells

Neuron1.ft <- subset(Neurons1, subset = nFeature_RNA > 250 & nCount_RNA > 250 & nCount_RNA < 10000) 
Neuron1.ft

# 33541 features across 1833 samples


```

Neurons 2 - CD56++

```{r}

VlnPlot(Neurons2, pt.size = 0.10, features = c("nFeature_RNA", "nCount_RNA", "percent.mt"), ncol = 3)
VlnPlot(Neurons2, pt.size = 0.10, features = c("nFeature_RNA"), y.max = 500)
VlnPlot(Neurons2, pt.size = 0.10, features = c("nFeature_RNA"), y.max = 1000)
VlnPlot(Neurons2, pt.size = 0.10, features = c("nCount_RNA"), y.max = 2000)



# filter more cells

Neuron2.ft <- subset(Neurons2, subset = nFeature_RNA > 500 & nCount_RNA > 500 & nCount_RNA < 10000) 
Neuron2.ft



```

Glia1 - Astrocyte data

```{r}

VlnPlot(Glia1, pt.size = 0.10, features = c("nFeature_RNA", "nCount_RNA", "percent.mt"), ncol = 3)

VlnPlot(Glia1, pt.size = 0.10, features = c("nFeature_RNA"), y.max = 5000)
VlnPlot(Glia1, pt.size = 0.10, features = c("nFeature_RNA"), y.max = 1000)
VlnPlot(Glia1, pt.size = 0.10, features = c("nCount_RNA"), y.max = 1000)
VlnPlot(Glia1, pt.size = 0.10, features = c("nCount_RNA"), y.max = 12000)


# extreme filter

Glia1.ft <- subset(Glia1, subset = nFeature_RNA > 500 & nCount_RNA > 300 & nCount_RNA < 10000) 
Glia1.ft
Glia1

VlnPlot(Glia1.ft, pt.size = 0.10, features = c("nFeature_RNA", "nCount_RNA", "percent.mt"), ncol = 3)





```


Glia2 - Radial Glia

```{r}

## Filter Glia 2
VlnPlot(Glia2, pt.size = 0.10, features = c("nFeature_RNA", "nCount_RNA", "percent.mt"), ncol = 3)

VlnPlot(Glia2, pt.size = 0.10, features = c("nFeature_RNA"), y.max = 5000)
VlnPlot(Glia2, pt.size = 0.10, features = c("nFeature_RNA"), y.max = 1000)
VlnPlot(Glia2, pt.size = 0.10, features = c("nCount_RNA"), y.max = 1000)
VlnPlot(Glia2, pt.size = 0.10, features = c("nCount_RNA"), y.max = 12000)


# extreme filter

Glia2.ft <- subset(Glia1, subset = nFeature_RNA > 500 & nCount_RNA > 500 & nCount_RNA < 10000) 
Glia2.ft


VlnPlot(Glia1.ft, pt.size = 0.10, features = c("nFeature_RNA", "nCount_RNA", "percent.mt"), ncol = 3)

# there are so many suposed cells I am concerned the high read cells are actually doublets. 



```


Analyze each dataset - get clusters 

```{r}

# cluster the neurons
seu <- Neuron1.ft
seu$orig.ident <- 'Neurons1'

seu <- NormalizeData(seu, normalization.method = "LogNormalize", scale.factor = 10000)
seu <- FindVariableFeatures(seu, selection.method = "vst", nfeatures = 2000)
# Identify the 10 most highly variable genes
top10 <- head(VariableFeatures(seu), 10)
# plot variable features with and without labels
plot1 <- VariableFeaturePlot(seu)
plot2 <- LabelPoints(plot = plot1, points = top10, repel = TRUE)



seu <- ScaleData(seu)
seu <- RunPCA(seu)
Idents(seu) <- 'orig.ident'
plot <- DimPlot(seu, reduction = "pca")


plot3 <- ElbowPlot(seu,ndims = 50)
plot3

plot2
plot



```

```{r}

# umap

seu <- RunUMAP(seu, reduction = "pca", n.neighbors = 43, dims = 1:25)
DimPlot(seu, reduction = "umap", group.by = "orig.ident")



```

Make the clusters Neurons1

```{r}

seu <- FindNeighbors(seu, dims = 1:25, k.param = 43)
seu <- FindClusters(seu, resolution = c(0,0.2,0.25,0.5,0.8))
seu <- FindClusters(seu, resolution = c(1.2))

library(clustree)
clustree(seu, prefix = "RNA_snn_res.")
DimPlot(seu)

```

```{r}
# look a lot at the clusers

VlnPlot(seu, features = c("nFeature_RNA", "nCount_RNA", "percent.mt"), group.by = 'seurat_clusters', ncol = 1)
# these cells might be grouping by - how much MT and number of Features
# clusters 4,5,6 have more features

VlnPlot(seu, features = c("nFeature_RNA", "nCount_RNA", "percent.mt"), group.by = 'RNA_snn_res.0.25', ncol = 1)
# here cluster 3 has higher expression, cluster 1 and 4 have similar Features RNA
# cluster 0 has higher percent MT levels

VlnPlot(seu, features = c("nFeature_RNA", "nCount_RNA", "percent.mt"), group.by = 'RNA_snn_res.0.5', ncol = 1)
VlnPlot(seu, features = c("nFeature_RNA", "nCount_RNA", "percent.mt"), group.by = 'RNA_snn_res.0.2', ncol = 1)
# now only cluster 0 have high MT and low features, clusters 1,2,3 have simiular RNA and Counts

```

Find the cluster markers for Neurons1

```{r}
Idents(seu) <- 'RNA_snn_res.0.2'
ClusterMarkers <- FindAllMarkers(seu)

top5 <- ClusterMarkers %>% group_by(cluster) %>% top_n(n=5, wt = avg_log2FC)
DoHeatmap(seu, features = top5$gene, size=3, angle =90, group.bar.height = 0.02, group.by = 'RNA_snn_res.0.2')


#write.csv(ClusterMarkers, "/Users/rhalenathomas/Documents/Data/scRNAseq/PhenoID/scRNAseqSorted/ClusterMarkers_neurons1_res025.csv")


write.csv(ClusterMarkers, "/Users/rhalenathomas/Documents/Data/scRNAseq/PhenoID/scRNAseqSorted/ClusterMarkers_neurons1_res02.csv")

```

```{r}
DimPlot(seu, group.by = 'RNA_snn_res.0.2', reduction = 'umap')

```


Get the most highly expressed genes in the total data (Neurons1)


```{r}

par(mar = c(4, 8, 2, 1))
C <- seu@assays$RNA@counts
C <- Matrix::t(Matrix::t(C)/Matrix::colSums(C)) * 100
most_expressed <- order(apply(C, 1, median), decreasing = T)[25:1]
boxplot(as.matrix(t(C[most_expressed, ])), cex = 0.1, las = 1, xlab = "% total count per cell",
    col = (scales::hue_pal())(25)[1:25], horizontal = TRUE)

# like in the tutorial I'm following MALAT1 is the top most expressed gene.  The top genes are a lot of MT and Ribosomal genes

seu[["percent.rb"]] <- PercentageFeatureSet(seu, pattern = "^RP")

VlnPlot(seu, features = "percent.rb", group.by = "RNA_snn_res.0.2")



```

Filters out specific genes

```{r}

seu.ft <- seu[!grepl("MALAT1", rownames(seu)), ]
seu.ft <- seu.ft[!grepl("^MT-", rownames(seu.ft)), ]

# this filtered object might cluster differently
# for now I'm going to move on to doublet detection


```



Try to find doublets with doublet finder

```{r}
remotes::install_github('chris-mcginnis-ucsf/DoubletFinder')
suppressMessages(require(DoubletFinder))


```

```{r}

seu.d = FindVariableFeatures(seu, verbose = F)
seu.d = ScaleData(seu.d, vars.to.regress = c("nFeature_RNA", "percent.mt"),
    verbose = F)
seu.d = RunPCA(seu.d, verbose = F, npcs = 20)
seu.d = RunUMAP(seu.d, dims = 1:10, verbose = F)

nExp <- round(ncol(seu.d) * 0.06)  # expect 6% doublets
seu.d <- doubletFinder_v3(seu.d, pN = 0.25, pK = 0.09, nExp = nExp, PCs = 1:10)


# name of the DF prediction can change, so extract the correct column name.
DF.name = colnames(seu.d@meta.data)[grepl("DF.classification", colnames(seu.d@meta.data))]



cowplot::plot_grid(ncol = 2, DimPlot(seu.d, group.by = "orig.ident") + NoAxes(),
    DimPlot(seu.d, group.by = DF.name) + NoAxes())


```

Do the double cells have more genes than the singlet??

```{r}

VlnPlot(seu.d, features = "nFeature_RNA", group.by = DF.name, pt.size = 0.1)


```

# remove the doublets

```{r}

seu.d <- seu.d[, seu.d@meta.data[, DF.name]== "Singlet"]
dim(seu.d)
dim(seu)

# removed about 100 cells




```


Save the filtered, doublet removed Neurons object 
Re-run PCA for clustering 

```{r}

saveRDS(seu.d, "/Users/rhalenathomas/Documents/Data/scRNAseq/PhenoID/scRNAseqSorted/objs/NeuronsFilteredSeu28092022.RDS")

```


DAsubgroups data has not be re-processed
Run standard workflow chunk

```{r}

#seu <- readRDS("/Users/rhalenathomas/Documents/Data/scRNAseq/Macosko_Data/PD_da.Rds")
#seu <- AIW60
seu <- NormalizeData(seu, normalization.method = "LogNormalize", scale.factor = 10000)
seu <- FindVariableFeatures(seu, selection.method = "vst", nfeatures = 2000)
seu <- ScaleData(seu)
seu <- RunPCA(seu)
seu <- RunUMAP(seu, reduction = "pca", n.neighbors = 159, dims = 1:30)
DimPlot(seu, reduction = "umap")

#saveRDS(seu, "/Users/rhalenathomas/Documents/Data/scRNAseq/Macosko_Data/DAsubgroups_processed.Rds")

#note my AIW 60 days data also didn't have the PCA saved 
# ran code chunck with n.neighbors = 123 
# saveRDS(seu, "/Users/rhalenathomas/Documents/Data/scRNAseq/AIWtrio60days/AWI002ParkinKOPinkKO60days_labels_14052022.rds")


```


Annotate clusters
Use: Organoid data, public brain data (LaManno, Lake, Mascako)


```{r}

# this is some reference data

# SNCA and control midbrain organoids 165 days in culture
MBO <- readRDS("/Users/rhalenathomas/Documents/Data/scRNAseq/AST23_BrainComm/MBOclusters_names29072021.rds")

# Midbrain  AIW002 120 days in culture
AIWMBO <- readRDS("/Users/rhalenathomas/Documents/Data/scRNAseq/AIWtrio120days/MOintegratedClusterK123res0.8.names_nov16_2021")

# Midbrain AIW002 60 days in culture

AIW60 <- readRDS("/Users/rhalenathomas/Documents/Data/scRNAseq/AIWtrio60days/AWI002ParkinKOPinkKO60days_labels_14052022.rds")

# DA neuron subtypes from postmortem brain Kamath et al 2022
DAsubtypes <- readRDS("/Users/rhalenathomas/Documents/Data/scRNAseq/Macosko_Data/DAsubgroups_processed.Rds")


# query
seu.q <- readRDS("/Users/rhalenathomas/Documents/Data/scRNAseq/PhenoID/scRNAseqSorted/objs/NeuronsFilteredSeu28092022.RDS")


#first predict with the MBO data
Idents(MBO) <- "cluster_labels"
DefaultAssay(MBO) <- "RNA"

# find the reference anchors
print("finding reference anchors")
anchors <- FindTransferAnchors(reference = MBO ,query = seu.q, dims = 1:25)
print("getting predictions")
predictions <- TransferData(anchorset = anchors, refdata = MBO$cluster_labels)
seu.q <- AddMetaData(seu.q, metadata = predictions)
print(table(seu.q$predicted.id))

Idents(seu.q) <- 'predicted.id'
# add new dataslot for MBO predicted ID to make the next prediction
seu.q$MBOAST23.pred <- Idents(seu.q)
DimPlot(seu.q, group.by = 'MBOAST23.pred', label = TRUE)
 
## check the proportion of cell types predicted in each cluster
t.lables <- as.data.frame(table(seu.q$RNA_snn_res.0.2, seu.q$predicted.id))
pr.t.lables <- as.data.frame(prop.table(table(seu.q$RNA_snn_res.0.2, seu.q$predicted.id)))
t.lables$Freq <- as.double(t.lables$Freq)


# try bar chart
ggplot(t.lables, aes(y = Freq, x = Var1, fill = Var2)) + geom_bar(position = "stack", stat= "identity")

# clusters don't break up by the predicted cell types

############ another predictions now using the AIW organoids

Idents(AIWMBO) <- "res08names"
DefaultAssay(AIWMBO) <- "RNA"

anchors <- FindTransferAnchors(reference = AIWMBO ,query = seu.q, dims = 1:25)
print("getting predictions")
predictions <- TransferData(anchorset = anchors, refdata = AIWMBO$res08names)
seu.q <- AddMetaData(seu.q, metadata = predictions)
print(table(seu.q$predicted.id))

Idents(seu.q) <- 'predicted.id'
# add new dataslot for MBO predicted ID to make the next prediction
seu.q$MBOAIW.pred <- Idents(seu.q)
DimPlot(seu.q, group.by = 'MBOAIW.pred', label = TRUE)
 
## check the proportion of cell types predicted in each cluster
t.lables <- as.data.frame(table(seu.q$RNA_snn_res.0.2, seu.q$predicted.id))
pr.t.lables <- as.data.frame(prop.table(table(seu.q$RNA_snn_res.0.2, seu.q$predicted.id)))
t.lables$Freq <- as.double(t.lables$Freq)


# try bar chart
ggplot(t.lables, aes(y = Freq, x = Var1, fill = Var2)) + geom_bar(position = "stack", stat= "identity")

# the predicted cell types make more sense from the AIW002 organoid
# now predict with the AIW002 60 days organoid

Idents(AIW60) <- "cluster.ids"
DefaultAssay(AIW60) <- "RNA"

anchors <- FindTransferAnchors(reference = AIW60, query = seu.q, dims = 1:25)
print("getting predictions")
predictions <- TransferData(anchorset = anchors, refdata = AIW60$cluster.ids) 
seu.q <- AddMetaData(seu.q, metadata = predictions)
print(table(seu.q$predicted.id))

Idents(seu.q) <- 'predicted.id'
# add new dataslot for MBO predicted ID to make the next prediction
seu.q$AIW60.pred <- Idents(seu.q)
DimPlot(seu.q, group.by = 'AIW60.pred', label = TRUE)
 
## check the proportion of cell types predicted in each cluster
t.lables <- as.data.frame(table(seu.q$RNA_snn_res.0.2, seu.q$predicted.id))
pr.t.lables <- as.data.frame(prop.table(table(seu.q$RNA_snn_res.0.2, seu.q$predicted.id)))
t.lables$Freq <- as.double(t.lables$Freq)


# try bar chart
ggplot(t.lables, aes(y = Freq, x = Var1, fill = Var2)) + geom_bar(position = "stack", stat= "identity")

# save ojbect with predicitons
saveRDS(seu.q, "/Users/rhalenathomas/Documents/Data/scRNAseq/PhenoID/scRNAseqSorted/objs/NeuronsFilteredSeu28092022.RDS")



```

```{r}
# compare the three predictions
#AST23 vs AIW60
t.lables <- as.data.frame(table(seu.q$MBOAST23.pred, seu.q$AIW60.pred))
pr.t.lables <- as.data.frame(prop.table(table(seu.q$RNA_snn_res.0.2, seu.q$predicted.id)))
t.lables$Freq <- as.double(t.lables$Freq)

# try bar chart
ggplot(t.lables, aes(y = Freq, x = Var1, fill = Var2)) + geom_bar(position = "stack", stat= "identity") + RotatedAxis()

#AST23 vs AIW120
t.lables <- as.data.frame(table(seu.q$MBOAST23.pred, seu.q$MBOAIW.pred))
pr.t.lables <- as.data.frame(prop.table(table(seu.q$RNA_snn_res.0.2, seu.q$predicted.id)))
t.lables$Freq <- as.double(t.lables$Freq)

# try bar chart
ggplot(t.lables, aes(y = Freq, x = Var1, fill = Var2)) + geom_bar(position = "stack", stat= "identity") + RotatedAxis()


# AIW60 vs AIW120
t.lables <- as.data.frame(table(seu.q$AIW60.pred, seu.q$MBOAIW.pred))
pr.t.lables <- as.data.frame(prop.table(table(seu.q$RNA_snn_res.0.2, seu.q$predicted.id)))
t.lables$Freq <- as.double(t.lables$Freq)

# try bar chart
ggplot(t.lables, aes(y = Freq, x = Var1, fill = Var2)) + geom_bar(position = "stack", stat= "identity") + RotatedAxis()


```

See how each looks on UMAP

```{r}

DimPlot(seu.q, group.by = 'RNA_snn_res.1.2')
DimPlot(seu.q, group.by = 'RNA_snn_res.0.2')
DimPlot(seu.q, group.by = 'AIW60.pred')
DimPlot(seu.q, group.by = 'MBOAIW.pred')
DimPlot(seu.q, group.by = 'MBOAST23.pred')


```

```{r}

# redo clusters
seu.q <- NormalizeData(seu.q, normalization.method = "LogNormalize", scale.factor = 10000)
seu.q <- FindVariableFeatures(seu.q, selection.method = "vst", nfeatures = 2000)
seu.q <- ScaleData(seu.q)
seu.q <- RunPCA(seu.q)
seu.q <- RunUMAP(seu.q, reduction = "pca", n.neighbors = 25, dims = 1:30, min.dist = 0.25, spread = 2)
DimPlot(seu.q, reduction = "umap", group.by = 'MBOAIW.pred')





```


Redo find clusters

```{r}

seu.q <- FindNeighbors(seu.q, dims = 1:25, k.param = 43)
seu.q <- FindClusters(seu.q, resolution = c(0,0.2,0.4,0.6))
seu.q <- FindClusters(seu.q, resolution = c(1.2))

library(clustree)
clustree(seu.q, prefix = "RNA_snn_res.")
DimPlot(seu.q)
```

Look at the predictions in the new clusters

```{r}
# AIW002 160 days predictions
t.lables <- as.data.frame(table(seu.q$RNA_snn_res.0.6, seu.q$MBOAIW.pred))
t.lables$Freq <- as.double(t.lables$Freq)
ggplot(t.lables, aes(y = Freq, x = Var1, fill = Var2)) + geom_bar(position = "stack", stat= "identity") + RotatedAxis() 
top.pred.celltype.AIW120 <- as.data.frame(t.lables  %>% group_by(Var1)  %>% top_n(1, Freq))

# AIW002 160 days predictions
t.lables <- as.data.frame(table(seu.q$RNA_snn_res.0.6, seu.q$AIW60.pred))
t.lables$Freq <- as.double(t.lables$Freq)
ggplot(t.lables, aes(y = Freq, x = Var1, fill = Var2)) + geom_bar(position = "stack", stat= "identity") + RotatedAxis() 
top.pred.celltype.AIW60 <-as.data.frame(t.lables  %>% group_by(Var1)  %>% top_n(1, Freq))

# AST23 65 days predictions
t.lables <- as.data.frame(table(seu.q$RNA_snn_res.0.6, seu.q$MBOAST23.pred))
t.lables$Freq <- as.double(t.lables$Freq)
ggplot(t.lables, aes(y = Freq, x = Var1, fill = Var2)) + geom_bar(position = "stack", stat= "identity") + RotatedAxis() 
top.pred.celltype.AST23 <- as.data.frame(t.lables  %>% group_by(Var1)  %>% top_n(1, Freq))


pred.table <- merge(top.pred.celltype.AIW120,top.pred.celltype.AIW60, by = 'Var1')
pred.table <- merge(pred.table, top.pred.celltype.AST23, by = 'Var1')
pred.table

```

Based on the 3 different predictions I can lable the cell types

0 - NPC or early neurons
1 - immature excitatory neurons
2 - NPC or early neurons
3 - RG or Oligos
4- Dopaminergic neurons - possibly early
5 - NPC or early neurons
6 - Radial GLia

I will also find markers and look at a list of neuronal markers

```{r}

Idents(seu.q) <- 'RNA_snn_res.0.6'
ClusterMarkers <- FindAllMarkers(seu.q, only.pos = TRUE)

top5 <- ClusterMarkers %>% group_by(cluster) %>% top_n(n=5, wt = avg_log2FC)
DoHeatmap(seu.q, features = top5$gene, size=3, angle =90, group.bar.height = 0.02, group.by = 'RNA_snn_res.0.6')

write.csv(ClusterMarkers,"/Users/rhalenathomas/Documents/Data/scRNAseq/PhenoID/scRNAseqSorted/Neurons1ClusterMarkers7.csv")


```

Explore some Gene expression levels

```{r}
feature_list = c("MKI67","SOX2","POU5F1","DLX2","PAX6","SOX9","HES1","NES","RBFOX3","MAP2","NCAM1","CD24","GRIA2","GRIN2B","GABBR1","GAD1","GAD2","GABRA1","GABRB2","TH","ALDH1A1","LMX1B","NR4A2","CORIN","CALB1","KCNJ6","CXCR4","ITGA6","SLC1A3","CD44","AQP4","S100B", "PDGFRA","OLIG2","MBP","CLDN11","VIM","VCAM1")

DoHeatmap(seu.q, features = feature_list, size=3, angle =90, group.bar.height = 0.02, group.by = 'RNA_snn_res.0.6')
DotPlot(seu.q, features = feature_list) +RotatedAxis()

PD_poulin = c("TH","SLC6A3","SLC18A2","SOX6","NDNF","SNCG","ALDH1A1","CALB1","TACR2","SLC17A6","SLC32A1","OTX2","GRP","LPL","CCK","VIP")

DoHeatmap(seu.q, features = PD_poulin, size=3, angle =90, group.bar.height = 0.02, group.by = 'RNA_snn_res.0.6')
DotPlot(seu.q, features = PD_poulin)+RotatedAxis()

ealryNeur = c("DCX","NEUROD1","TBR1")
proliferation = c("PCNA","MKI67")
neuralstem = c("SOX2","NES","PAX6","MASH1")

feature_list <- c("DCX","NEUROD1","TBR1","PCNA","MKI67","SOX2","NES","PAX6","MASH1")
DoHeatmap(seu.q, features = feature_list, size=3, angle =90, group.bar.height = 0.02, group.by = 'RNA_snn_res.0.6')
DotPlot(seu.q, features = feature_list)+RotatedAxis()
# no proliferation marker expression  PCNA or MKI67
# cluster 4 DA neurons - shows early neuron marker and low PAX 4
# cluster 3 has higher SOX2 - neuroblast marker / NPC marker

mat_neuron = c("RBFOX3","SYP","DLG45","VAMP1","VAMP2","TUBB3","SYT1","BSN","HOMER1","SLC17A6") 
# NeuN is FOX3 - RBFOX3
# PSD95 also SP-90 or DLG4
# VGLUT2 is SLC17A6
DoHeatmap(seu.q, features = mat_neuron, size=3, angle =90, group.bar.height = 0.02, group.by = 'RNA_snn_res.0.6')
# cluster 4 also show mature neuron markers
DotPlot(seu.q, features = mat_neuron)+RotatedAxis()
# excitatory neuron markers
ex = c("GRIA2","GRIA1","GRIA4","GRIN1","GRIN2B","GRIN2A","GRIN3A","GRIN3","GRIP1","CAMK2A")
DoHeatmap(seu.q, features = ex, size=3, angle =90, group.bar.height = 0.02, group.by = 'RNA_snn_res.0.6')
DotPlot(seu.q, features = ex)+RotatedAxis()
# inhibitory neuron markers
inh = c("GAD1","GAD2", "GAT1","PVALB","GABR2","GABR1","GBRR1","GABRB2","GABRB1","GABRB3","GABRA6","GABRA1","GABRA4","TRAK2")
DoHeatmap(seu.q, features = inh, size=3, angle =90, group.bar.height = 0.02, group.by = 'RNA_snn_res.0.6')
DotPlot(seu.q, features = inh)+RotatedAxis()
# cluster 4 is more excitatory than inhbitory but neither marker set has much expression 



```


Checkout the Enricher cell type libraries from 

```{r}
# test markers for the 7 clusters in Neurons1 

library(devtools)
install_github("wjawaid/enrichR")
library(enrichR)


setEnrichrSite("Enrichr") # Human genes
# list of all the databases

dbs <- listEnrichrDbs()
dbs
# libaries with cell types

db <- c('Allen_Brain_Atlas_up','Descartes_Cell_Types_and_Tissue_2021',
        'CellMarker_Augmented_2021','Azimuth_Cell_Types_2021')

# enrichr(genes, databases = NULL)

N1.c0 <- ClusterMarkers %>% filter(cluster == 0 & avg_log2FC > 0)
genes <- N1.c0$gene

N1.c0.Er <- enrichr(genes, databases = db)
plotEnrich(N1.c0.Er[[1]], showTerms = 20, numChar = 40, y = "Count", orderBy = "P.value")
plotEnrich(N1.c0.Er[[2]], showTerms = 20, numChar = 40, y = "Count", orderBy = "P.value")
plotEnrich(N1.c0.Er[[3]], showTerms = 20, numChar = 40, y = "Count", orderBy = "P.value")

N1.Er.genes.1 <- N1.c0.Er[[1]] %>% select(Term, Genes, Combined.Score)
N1.Er.genes.1

N1.Er.genes.2 <- N1.c0.Er[[2]] %>% select(Term, Genes, Combined.Score)
N1.Er.genes.2

N1.Er.genes.3 <- N1.c0.Er[[3]] %>% select(Term, Genes, Combined.Score)
N1.Er.genes.3

# cluster 0 could be hypothalmus, DA neurons A13

N1.c1 <- ClusterMarkers %>% filter(cluster == 1 & avg_log2FC > 0)
genes <- N1.c1$gene

N1.c1.Er <- enrichr(genes, databases = db)
plotEnrich(N1.c1.Er[[1]], showTerms = 20, numChar = 40, y = "Count", orderBy = "P.value")
plotEnrich(N1.c1.Er[[2]], showTerms = 20, numChar = 40, y = "Count", orderBy = "P.value")
plotEnrich(N1.c1.Er[[3]], showTerms = 20, numChar = 40, y = "Count", orderBy = "P.value")
plotEnrich(N1.c1.Er[[4]], showTerms = 20, numChar = 40, y = "Count", orderBy = "P.value")

N1.Er.genes.1 <- N1.c1.Er[[1]] %>% select(Term, Genes, Combined.Score)
N1.Er.genes.1

N1.Er.genes.2 <- N1.c1.Er[[2]] %>% select(Term, Genes, Combined.Score)
N1.Er.genes.2

N1.Er.genes.3 <- N1.c1.Er[[3]] %>% select(Term, Genes, Combined.Score)
N1.Er.genes.3

N1.Er.genes.4 <- N1.c1.Er[[4]] %>% select(Term, Genes, Combined.Score)
N1.Er.genes.4

# cluster 1; olfactory bulb, neural plate, maybe Radial Glia, 
N1.c2 <- ClusterMarkers %>% filter(cluster == 2 & avg_log2FC > 0)
genes <- N1.c2$gene

N1.c2.Er <- enrichr(genes, databases = db)
plotEnrich(N1.c2.Er[[1]], showTerms = 20, numChar = 40, y = "Count", orderBy = "P.value")
plotEnrich(N1.c2.Er[[2]], showTerms = 20, numChar = 40, y = "Count", orderBy = "P.value")
plotEnrich(N1.c2.Er[[3]], showTerms = 20, numChar = 40, y = "Count", orderBy = "P.value")
plotEnrich(N1.c2.Er[[4]], showTerms = 20, numChar = 40, y = "Count", orderBy = "P.value")

N1.Er.genes.1 <- N1.c2.Er[[1]] %>% select(Term, Genes, Combined.Score)
N1.Er.genes.1

N1.Er.genes.2 <- N1.c2.Er[[2]] %>% select(Term, Genes, Combined.Score)
N1.Er.genes.2

N1.Er.genes.3 <- N1.c2.Er[[3]] %>% select(Term, Genes, Combined.Score)
N1.Er.genes.3

N1.Er.genes.4 <- N1.c2.Er[[4]] %>% select(Term, Genes, Combined.Score)
N1.Er.genes.4

# cluster 2 some brain nucleus, neural stem

N1.c3 <- ClusterMarkers %>% filter(cluster == 3 & avg_log2FC > 0)
genes <- N1.c3$gene

N1.c3.Er <- enrichr(genes, databases = db)
plotEnrich(N1.c3.Er[[1]], showTerms = 20, numChar = 40, y = "Count", orderBy = "P.value")
plotEnrich(N1.c3.Er[[2]], showTerms = 20, numChar = 40, y = "Count", orderBy = "P.value")
plotEnrich(N1.c3.Er[[3]], showTerms = 20, numChar = 40, y = "Count", orderBy = "P.value")
plotEnrich(N1.c3.Er[[4]], showTerms = 20, numChar = 40, y = "Count", orderBy = "P.value")

N1.Er.genes.1 <- N1.c3.Er[[1]] %>% select(Term, Genes, Combined.Score)
N1.Er.genes.1

N1.Er.genes.2 <- N1.c3.Er[[2]] %>% select(Term, Genes, Combined.Score)
N1.Er.genes.2

N1.Er.genes.3 <- N1.c3.Er[[3]] %>% select(Term, Genes, Combined.Score)
N1.Er.genes.3

N1.Er.genes.4 <- N1.c3.Er[[4]] %>% select(Term, Genes, Combined.Score)
N1.Er.genes.4

# cluster 3 stromal cell of thymus, embryonic astrocytes, OPC, NK cells, monocytes

N1.c4 <- ClusterMarkers %>% filter(cluster == 4 & avg_log2FC > 0)
genes <- N1.c4$gene

N1.c4.Er <- enrichr(genes, databases = db)
plotEnrich(N1.c4.Er[[1]], showTerms = 20, numChar = 40, y = "Count", orderBy = "P.value")
plotEnrich(N1.c4.Er[[2]], showTerms = 20, numChar = 40, y = "Count", orderBy = "P.value")
plotEnrich(N1.c4.Er[[3]], showTerms = 20, numChar = 40, y = "Count", orderBy = "P.value")
plotEnrich(N1.c4.Er[[4]], showTerms = 20, numChar = 40, y = "Count", orderBy = "P.value")

N1.Er.genes.1 <- N1.c4.Er[[1]] %>% select(Term, Genes, Combined.Score)
N1.Er.genes.1

N1.Er.genes.2 <- N1.c4.Er[[2]] %>% select(Term, Genes, Combined.Score)
N1.Er.genes.2

N1.Er.genes.3 <- N1.c4.Er[[3]] %>% select(Term, Genes, Combined.Score)
N1.Er.genes.3

N1.Er.genes.4 <- N1.c4.Er[[4]] %>% select(Term, Genes, Combined.Score)
N1.Er.genes.4

# Dentate gyrus - different cortical layers, neurons, neurons, NPC, neurons GABA,GLUT

N1.c5 <- ClusterMarkers %>% filter(cluster == 5 & avg_log2FC > 0)
genes <- N1.c5$gene

N1.c5.Er <- enrichr(genes, databases = db)
plotEnrich(N1.c5.Er[[1]], showTerms = 20, numChar = 40, y = "Count", orderBy = "P.value")
plotEnrich(N1.c5.Er[[2]], showTerms = 20, numChar = 40, y = "Count", orderBy = "P.value")
plotEnrich(N1.c5.Er[[3]], showTerms = 20, numChar = 40, y = "Count", orderBy = "P.value")
plotEnrich(N1.c5.Er[[4]], showTerms = 20, numChar = 40, y = "Count", orderBy = "P.value")

N1.Er.genes.1 <- N1.c5.Er[[1]] %>% select(Term, Genes, Combined.Score)
N1.Er.genes.1

N1.Er.genes.2 <- N1.c5.Er[[2]] %>% select(Term, Genes, Combined.Score)
N1.Er.genes.2

N1.Er.genes.3 <- N1.c5.Er[[3]] %>% select(Term, Genes, Combined.Score)
N1.Er.genes.3

N1.Er.genes.4 <- N1.c5.Er[[4]] %>% select(Term, Genes, Combined.Score)
N1.Er.genes.4

# cluster 5 hippocampus, endothelial cells, pericytes

N1.c6 <- ClusterMarkers %>% filter(cluster == 6 & avg_log2FC > 0)
genes <- N1.c6$gene

N1.c6.Er <- enrichr(genes, databases = db)
plotEnrich(N1.c6.Er[[1]], showTerms = 20, numChar = 40, y = "Count", orderBy = "P.value")
plotEnrich(N1.c6.Er[[2]], showTerms = 20, numChar = 40, y = "Count", orderBy = "P.value")
plotEnrich(N1.c6.Er[[3]], showTerms = 20, numChar = 40, y = "Count", orderBy = "P.value")
plotEnrich(N1.c6.Er[[4]], showTerms = 20, numChar = 40, y = "Count", orderBy = "P.value")

N1.Er.genes.1 <- N1.c6.Er[[1]] %>% select(Term, Genes, Combined.Score)
N1.Er.genes.1

N1.Er.genes.2 <- N1.c6.Er[[2]] %>% select(Term, Genes, Combined.Score)
N1.Er.genes.2

N1.Er.genes.3 <- N1.c6.Er[[3]] %>% select(Term, Genes, Combined.Score)
N1.Er.genes.3

N1.Er.genes.4 <- N1.c6.Er[[4]] %>% select(Term, Genes, Combined.Score)
N1.Er.genes.4

# cluster 6 brain cortex, shwann cell, endothelial, pericyte, GABA


```

Library of tissue cell types for up regulated genes per cluster
0 - hypothalmus, DA A13
1- neural plate, Radial Glia
2 - Neural stem
3 - stromal, astro OPC
4 - Neurons
5 - endothelial, pericyte
6 - maybe neurons maybe not


By the combined information - annotate the clusters in Neurons1

```{r}
#Based on the 3 different predictions I can lable the cell types

#0 - NPC or early neurons
#1 - immature excitatory neurons
#2 - NPC or early neurons
#3 - RG or Oligos
#4- Dopaminergic neurons - possibly early
#5 - NPC or early neurons
#6 - Radial Glia

Idents(seu.q) <- 'RNA_snn_res.0.6'
cluster.ids <- c("ImmatureNeurons","Neurons","NPC","OPC-RG","DAneurons",
                 "Other","RG")
unique(seu.q$RNA_snn_res.0.6)

names(cluster.ids) <- levels(seu.q)
seu.q <- RenameIdents(seu.q, cluster.ids)
seu.q$subgroups <- Idents(seu.q)

DimPlot(seu.q, reduction = "umap", label = TRUE, group.by = 'subgroups', repel = TRUE)


saveRDS(seu.q, "/Users/rhalenathomas/Documents/Data/scRNAseq/PhenoID/scRNAseqSorted/objs/Neuron1LabledSeu30092022.RDS")

```


### Next Repeat everything for Neurons2

```{r}
# explore filtering
seu <- Neurons2
seu
VlnPlot(seu, pt.size = 0.10, features = c("nFeature_RNA", "nCount_RNA", "percent.mt"), ncol = 3)

VlnPlot(seu, pt.size = 0.10, features = c("nFeature_RNA"), y.max = 2000)
VlnPlot(seu, pt.size = 0.10, features = c("nFeature_RNA"), y.max = 350)
VlnPlot(seu, pt.size = 0.10, features = c("nCount_RNA"), y.max = 2000)

# filter more cells

seu.ft <- subset(seu, subset = nFeature_RNA > 300 & nCount_RNA > 500 & nCount_RNA < 10000) 
seu.ft

# 17604 samples with 250 nFeature_RNA
# 9657 with  nFeature 300 and nCOunt 500


```


Doublet finder 

```{r}
suppressMessages(require(DoubletFinder))

# filtering out MALAT1 and mitochondrial genes

seu.ft <- seu.ft[!grepl("MALAT1", rownames(seu)), ]
seu.ft <- seu.ft[!grepl("^MT-", rownames(seu.ft)), ]

# like in the tutorial I'm following MALAT1 is the top most expressed gene.  The top genes are a lot of MT and Ribosomal genes

seu.ft[["percent.rb"]] <- PercentageFeatureSet(seu.ft, pattern = "^RP")

seu.d = NormalizeData(seu.ft)
seu.d = FindVariableFeatures(seu.d, verbose = F)
seu.d = ScaleData(seu.d, vars.to.regress = c("nFeature_RNA", "percent.mt"),
    verbose = F)
seu.d = RunPCA(seu.d, verbose = F, npcs = 30)
seu.d = RunUMAP(seu.d, dims = 1:10, verbose = F)

nExp <- round(ncol(seu.d) * 0.08)  # expect more doublets because there is a lot more cells
seu.d <- doubletFinder_v3(seu.d, pN = 0.25, pK = 0.09, nExp = nExp, PCs = 1:10)


# name of the DF prediction can change, so extract the correct column name.
DF.name = colnames(seu.d@meta.data)[grepl("DF.classification", colnames(seu.d@meta.data))]


cowplot::plot_grid(ncol = 2, DimPlot(seu.d, group.by = "orig.ident") + NoAxes(),
    DimPlot(seu.d, group.by = DF.name) + NoAxes())

VlnPlot(seu.d, features = "nFeature_RNA", group.by = DF.name, pt.size = 0.1)

```


Remove the doublet cells

```{r}

seu.d <- seu.d[, seu.d@meta.data[, DF.name]== "Singlet"]
dim(seu.d)
dim(seu)
# 9657 cells pre filter
# 8884 cells after filtering
# note the percent doubles expected is close to the percent detected

```


Repeat workflow with doublet removed data and find clusters for 

```{r}


seu <- NormalizeData(seu.d, normalization.method = "LogNormalize", scale.factor = 10000)
seu <- FindVariableFeatures(seu, selection.method = "vst", nfeatures = 2000)
seu <- ScaleData(seu)
seu <- RunPCA(seu)
seu <- RunUMAP(seu, reduction = "pca", n.neighbors = 43, dims = 1:30)
DimPlot(seu, reduction = "umap")

seu.q <- FindNeighbors(seu, dims = 1:25, k.param = 43)
seu.q <- FindClusters(seu.q, resolution = c(0,0.2,0.4,0.6))

library(clustree)
clustree(seu.q)

DimPlot(seu.q, reduction = "umap", group.by = 'RNA_snn_res.0.2')
DimPlot(seu.q, reduction = "umap", group.by = 'RNA_snn_res.0.4')
DimPlot(seu.q, reduction = "umap", group.by = 'RNA_snn_res.0.6')
DimPlot(seu.q, reduction = "umap", group.by = 'RNA_snn_res.1.2')

```

Label cell types using the label transfer

```{r}



# SNCA and control midbrain organoids 165 days in culture
MBO <- readRDS("/Users/rhalenathomas/Documents/Data/scRNAseq/AST23_BrainComm/MBOclusters_names29072021.rds")

# Midbrain  AIW002 120 days in culture
AIWMBO <- readRDS("/Users/rhalenathomas/Documents/Data/scRNAseq/AIWtrio120days/MOintegratedClusterK123res0.8.names_nov16_2021")

# Midbrain AIW002 60 days in culture

AIW60 <- readRDS("/Users/rhalenathomas/Documents/Data/scRNAseq/AIWtrio60days/AWI002ParkinKOPinkKO60days_labels_14052022.rds")


# query
#seu.q <- readRDS("/Users/rhalenathomas/Documents/Data/scRNAseq/PhenoID/scRNAseqSorted/objs/NeuronsFilteredSeu28092022.RDS")


#first predict with the MBO data
Idents(MBO) <- "cluster_labels"
DefaultAssay(MBO) <- "RNA"

# find the reference anchors
print("finding reference anchors")
anchors <- FindTransferAnchors(reference = MBO ,query = seu.q, dims = 1:25)
print("getting predictions")
predictions <- TransferData(anchorset = anchors, refdata = MBO$cluster_labels)
seu.q <- AddMetaData(seu.q, metadata = predictions)
print(table(seu.q$predicted.id))

Idents(seu.q) <- 'predicted.id'
# add new dataslot for MBO predicted ID to make the next prediction
seu.q$MBOAST23.pred <- Idents(seu.q)
DimPlot(seu.q, group.by = 'MBOAST23.pred', label = TRUE)
 
## check the proportion of cell types predicted in each cluster
t.lables <- as.data.frame(table(seu.q$RNA_snn_res.0.2, seu.q$predicted.id))
pr.t.lables <- as.data.frame(prop.table(table(seu.q$RNA_snn_res.0.2, seu.q$predicted.id)))
t.lables$Freq <- as.double(t.lables$Freq)


# try bar chart
ggplot(t.lables, aes(y = Freq, x = Var1, fill = Var2)) + geom_bar(position = "stack", stat= "identity")

# clusters don't break up by the predicted cell types

############ another predictions now using the AIW organoids

Idents(AIWMBO) <- "res08names"
DefaultAssay(AIWMBO) <- "RNA"

anchors <- FindTransferAnchors(reference = AIWMBO ,query = seu.q, dims = 1:25)
print("getting predictions")
predictions <- TransferData(anchorset = anchors, refdata = AIWMBO$res08names)
seu.q <- AddMetaData(seu.q, metadata = predictions)
print(table(seu.q$predicted.id))

Idents(seu.q) <- 'predicted.id'
# add new dataslot for MBO predicted ID to make the next prediction
seu.q$MBOAIW.pred <- Idents(seu.q)
DimPlot(seu.q, group.by = 'MBOAIW.pred', label = TRUE)
 
## check the proportion of cell types predicted in each cluster
t.lables <- as.data.frame(table(seu.q$RNA_snn_res.0.2, seu.q$predicted.id))
pr.t.lables <- as.data.frame(prop.table(table(seu.q$RNA_snn_res.0.2, seu.q$predicted.id)))
t.lables$Freq <- as.double(t.lables$Freq)


# try bar chart
ggplot(t.lables, aes(y = Freq, x = Var1, fill = Var2)) + geom_bar(position = "stack", stat= "identity")

# the predicted cell types make more sense from the AIW002 organoid
# now predict with the AIW002 60 days organoid

Idents(AIW60) <- "cluster.ids"
DefaultAssay(AIW60) <- "RNA"

anchors <- FindTransferAnchors(reference = AIW60, query = seu.q, dims = 1:25)
print("getting predictions")
predictions <- TransferData(anchorset = anchors, refdata = AIW60$cluster.ids) 
seu.q <- AddMetaData(seu.q, metadata = predictions)
print(table(seu.q$predicted.id))

Idents(seu.q) <- 'predicted.id'
# add new dataslot for MBO predicted ID to make the next prediction
seu.q$AIW60.pred <- Idents(seu.q)
DimPlot(seu.q, group.by = 'AIW60.pred', label = TRUE)
 
## check the proportion of cell types predicted in each cluster
t.lables <- as.data.frame(table(seu.q$RNA_snn_res.0.2, seu.q$predicted.id))
pr.t.lables <- as.data.frame(prop.table(table(seu.q$RNA_snn_res.0.2, seu.q$predicted.id)))
t.lables$Freq <- as.double(t.lables$Freq)


# try bar chart
ggplot(t.lables, aes(y = Freq, x = Var1, fill = Var2)) + geom_bar(position = "stack", stat= "identity")

# save ojbect with predicitons
saveRDS(seu.q, "/Users/rhalenathomas/Documents/Data/scRNAseq/PhenoID/scRNAseqSorted/objs/Neurons2PredictionsSeu30092022.RDS")



```

See the top predictions for each cluster in Neurons2 res 06 


```{r}

# AIW002 120 days predictions
t.lables <- as.data.frame(table(seu.q$RNA_snn_res.0.6, seu.q$MBOAIW.pred))
t.lables$Freq <- as.double(t.lables$Freq)
ggplot(t.lables, aes(y = Freq, x = Var1, fill = Var2)) + geom_bar(position = "stack", stat= "identity") + RotatedAxis() 
top.pred.celltype.AIW120 <- as.data.frame(t.lables  %>% group_by(Var1)  %>% top_n(2, Freq))
df.top.aiw120 <- top.pred.celltype.AIW120[order(top.pred.celltype.AIW120$Var1,-top.pred.celltype.AIW120$Freq),]
row.names(df.top.aiw120) <- NULL
df.top.aiw120$I <- row.names(df.top.aiw120)

# AIW002 60 days predictions
t.lables <- as.data.frame(table(seu.q$RNA_snn_res.0.6, seu.q$AIW60.pred))
t.lables$Freq <- as.double(t.lables$Freq)
ggplot(t.lables, aes(y = Freq, x = Var1, fill = Var2)) + geom_bar(position = "stack", stat= "identity") + RotatedAxis() 
top.pred.celltype.AIW60 <-as.data.frame(t.lables  %>% group_by(Var1)  %>% top_n(2, Freq))
df.top.aiw60 <- top.pred.celltype.AIW60[order(top.pred.celltype.AIW60$Var1,-top.pred.celltype.AIW60$Freq),]
row.names(df.top.aiw60) <- NULL
df.top.aiw60$I <- row.names(df.top.aiw60)


# AST23 165 days predictions
t.lables <- as.data.frame(table(seu.q$RNA_snn_res.0.6, seu.q$MBOAST23.pred))
t.lables$Freq <- as.double(t.lables$Freq)
ggplot(t.lables, aes(y = Freq, x = Var1, fill = Var2)) + geom_bar(position = "stack", stat= "identity") + RotatedAxis() 
top.pred.celltype.AST23 <- as.data.frame(t.lables  %>% group_by(Var1)  %>% top_n(2, Freq))
df.top.AST23 <- top.pred.celltype.AST23[order(top.pred.celltype.AST23$Var1,-top.pred.celltype.AST23$Freq),]
row.names(df.top.AST23) <- NULL
df.top.AST23$I <- row.names(df.top.AST23)

pred.table <- merge(df.top.AST23, df.top.aiw60, by = 'I', all = TRUE)
pred.table <- merge(pred.table, df.top.aiw120, by = 'I')
pred.table

```

What cell types are predicted across the 3 references

0 - Neurons early , NPC, neurons excitatory
1 - Neurons early, NPC
2 - Neurons early, NPC, neurons excitatory some DA neurons
3 - Oligo, RG, 
4 - Excitatory neurons, NPC, early neurons
5 - DA neurons, early DA neurons
6 - neurons immature NPC
7 - DA neurons
8 - RG, oligo, OPC, NPC
9 - Radial Glia
10 - NPC, neurons, oligo
11 - NPC, neurons, oligo


Find cluster markers and see how those would annotate

```{r}
Idents(seu.q) <- 'RNA_snn_res.0.6'
ClusterMarkers <- FindAllMarkers(seu.q, only.pos = TRUE)

top5 <- ClusterMarkers %>% group_by(cluster) %>% top_n(n=5, wt = avg_log2FC)
DoHeatmap(seu.q, features = top5$gene, size=3, angle =90, group.bar.height = 0.02, group.by = 'RNA_snn_res.0.6')

write.csv(ClusterMarkers,"/Users/rhalenathomas/Documents/Data/scRNAseq/PhenoID/scRNAseqSorted/Neurons1ClusterMarkers11.csv")

```
Cluster 0 has fewer markers. 
2 and 5 have similar up reg markers
3 and 4 also overlap



Look at the cluster markers in cell type libraries for Neurons 2

```{r}
library(enrichR)
db <- c('Allen_Brain_Atlas_up','Descartes_Cell_Types_and_Tissue_2021',
        'CellMarker_Augmented_2021','Azimuth_Cell_Types_2021')

# enrichr(genes, databases = NULL)
# cluster 0

N1.c0 <- ClusterMarkers %>% filter(cluster == 0 & avg_log2FC > 0)
genes <- N1.c0$gene

N1.c0.Er <- enrichr(genes, databases = db)
plotEnrich(N1.c0.Er[[1]], showTerms = 20, numChar = 40, y = "Count", orderBy = "P.value")
plotEnrich(N1.c0.Er[[2]], showTerms = 20, numChar = 40, y = "Count", orderBy = "P.value")
plotEnrich(N1.c0.Er[[3]], showTerms = 20, numChar = 40, y = "Count", orderBy = "P.value")

N1.Er.genes.1 <- N1.c0.Er[[1]] %>% select(Term, Genes, Combined.Score)
N1.Er.genes.1

N1.Er.genes.2 <- N1.c0.Er[[2]] %>% select(Term, Genes, Combined.Score)
N1.Er.genes.2

N1.Er.genes.3 <- N1.c0.Er[[3]] %>% select(Term, Genes, Combined.Score)
N1.Er.genes.3

# cluster 1

N1.c1 <- ClusterMarkers %>% filter(cluster == 1 & avg_log2FC > 0)
genes <- N1.c1$gene

N1.c1.Er <- enrichr(genes, databases = db)
plotEnrich(N1.c1.Er[[1]], showTerms = 20, numChar = 40, y = "Count", orderBy = "P.value")
plotEnrich(N1.c1.Er[[2]], showTerms = 20, numChar = 40, y = "Count", orderBy = "P.value")
plotEnrich(N1.c1.Er[[3]], showTerms = 20, numChar = 40, y = "Count", orderBy = "P.value")
plotEnrich(N1.c1.Er[[4]], showTerms = 20, numChar = 40, y = "Count", orderBy = "P.value")

N1.Er.genes.1 <- N1.c1.Er[[1]] %>% select(Term, Genes, Combined.Score)
N1.Er.genes.1

N1.Er.genes.2 <- N1.c1.Er[[2]] %>% select(Term, Genes, Combined.Score)
N1.Er.genes.2

N1.Er.genes.3 <- N1.c1.Er[[3]] %>% select(Term, Genes, Combined.Score)
N1.Er.genes.3

N1.Er.genes.4 <- N1.c1.Er[[4]] %>% select(Term, Genes, Combined.Score)
N1.Er.genes.4

# cluster 1; olfactory bulb, neural plate, maybe Radial Glia, 
N1.c2 <- ClusterMarkers %>% filter(cluster == 2 & avg_log2FC > 0)
genes <- N1.c2$gene

N1.c2.Er <- enrichr(genes, databases = db)
plotEnrich(N1.c2.Er[[1]], showTerms = 20, numChar = 40, y = "Count", orderBy = "P.value")
plotEnrich(N1.c2.Er[[2]], showTerms = 20, numChar = 40, y = "Count", orderBy = "P.value")
plotEnrich(N1.c2.Er[[3]], showTerms = 20, numChar = 40, y = "Count", orderBy = "P.value")
plotEnrich(N1.c2.Er[[4]], showTerms = 20, numChar = 40, y = "Count", orderBy = "P.value")

N1.Er.genes.1 <- N1.c2.Er[[1]] %>% select(Term, Genes, Combined.Score)
N1.Er.genes.1

N1.Er.genes.2 <- N1.c2.Er[[2]] %>% select(Term, Genes, Combined.Score)
N1.Er.genes.2

N1.Er.genes.3 <- N1.c2.Er[[3]] %>% select(Term, Genes, Combined.Score)
N1.Er.genes.3

N1.Er.genes.4 <- N1.c2.Er[[4]] %>% select(Term, Genes, Combined.Score)
N1.Er.genes.4

# cluster 3

N1.c3 <- ClusterMarkers %>% filter(cluster == 3 & avg_log2FC > 0)
genes <- N1.c3$gene

N1.c3.Er <- enrichr(genes, databases = db)
plotEnrich(N1.c3.Er[[1]], showTerms = 20, numChar = 40, y = "Count", orderBy = "P.value")
plotEnrich(N1.c3.Er[[2]], showTerms = 20, numChar = 40, y = "Count", orderBy = "P.value")
plotEnrich(N1.c3.Er[[3]], showTerms = 20, numChar = 40, y = "Count", orderBy = "P.value")
plotEnrich(N1.c3.Er[[4]], showTerms = 20, numChar = 40, y = "Count", orderBy = "P.value")

N1.Er.genes.1 <- N1.c3.Er[[1]] %>% select(Term, Genes, Combined.Score)
N1.Er.genes.1

N1.Er.genes.2 <- N1.c3.Er[[2]] %>% select(Term, Genes, Combined.Score)
N1.Er.genes.2

N1.Er.genes.3 <- N1.c3.Er[[3]] %>% select(Term, Genes, Combined.Score)
N1.Er.genes.3

N1.Er.genes.4 <- N1.c3.Er[[4]] %>% select(Term, Genes, Combined.Score)
N1.Er.genes.4

# cluster 4

N1.c4 <- ClusterMarkers %>% filter(cluster == 4 & avg_log2FC > 0)
genes <- N1.c4$gene

N1.c4.Er <- enrichr(genes, databases = db)
plotEnrich(N1.c4.Er[[1]], showTerms = 20, numChar = 40, y = "Count", orderBy = "P.value")
plotEnrich(N1.c4.Er[[2]], showTerms = 20, numChar = 40, y = "Count", orderBy = "P.value")
plotEnrich(N1.c4.Er[[3]], showTerms = 20, numChar = 40, y = "Count", orderBy = "P.value")
plotEnrich(N1.c4.Er[[4]], showTerms = 20, numChar = 40, y = "Count", orderBy = "P.value")

N1.Er.genes.1 <- N1.c4.Er[[1]] %>% select(Term, Genes, Combined.Score)
N1.Er.genes.1

N1.Er.genes.2 <- N1.c4.Er[[2]] %>% select(Term, Genes, Combined.Score)
N1.Er.genes.2

N1.Er.genes.3 <- N1.c4.Er[[3]] %>% select(Term, Genes, Combined.Score)
N1.Er.genes.3

N1.Er.genes.4 <- N1.c4.Er[[4]] %>% select(Term, Genes, Combined.Score)
N1.Er.genes.4

# cluster 5

N1.c5 <- ClusterMarkers %>% filter(cluster == 5 & avg_log2FC > 0)
genes <- N1.c5$gene

N1.c5.Er <- enrichr(genes, databases = db)
plotEnrich(N1.c5.Er[[1]], showTerms = 20, numChar = 40, y = "Count", orderBy = "P.value")
plotEnrich(N1.c5.Er[[2]], showTerms = 20, numChar = 40, y = "Count", orderBy = "P.value")
plotEnrich(N1.c5.Er[[3]], showTerms = 20, numChar = 40, y = "Count", orderBy = "P.value")
plotEnrich(N1.c5.Er[[4]], showTerms = 20, numChar = 40, y = "Count", orderBy = "P.value")

N1.Er.genes.1 <- N1.c5.Er[[1]] %>% select(Term, Genes, Combined.Score)
N1.Er.genes.1

N1.Er.genes.2 <- N1.c5.Er[[2]] %>% select(Term, Genes, Combined.Score)
N1.Er.genes.2

N1.Er.genes.3 <- N1.c5.Er[[3]] %>% select(Term, Genes, Combined.Score)
N1.Er.genes.3

N1.Er.genes.4 <- N1.c5.Er[[4]] %>% select(Term, Genes, Combined.Score)
N1.Er.genes.4

# cluster 6

N1.c6 <- ClusterMarkers %>% filter(cluster == 6 & avg_log2FC > 0)
genes <- N1.c6$gene

N1.c6.Er <- enrichr(genes, databases = db)
plotEnrich(N1.c6.Er[[1]], showTerms = 20, numChar = 40, y = "Count", orderBy = "P.value")
plotEnrich(N1.c6.Er[[2]], showTerms = 20, numChar = 40, y = "Count", orderBy = "P.value")
plotEnrich(N1.c6.Er[[3]], showTerms = 20, numChar = 40, y = "Count", orderBy = "P.value")
plotEnrich(N1.c6.Er[[4]], showTerms = 20, numChar = 40, y = "Count", orderBy = "P.value")

N1.Er.genes.1 <- N1.c6.Er[[1]] %>% select(Term, Genes, Combined.Score)
N1.Er.genes.1

N1.Er.genes.2 <- N1.c6.Er[[2]] %>% select(Term, Genes, Combined.Score)
N1.Er.genes.2

N1.Er.genes.3 <- N1.c6.Er[[3]] %>% select(Term, Genes, Combined.Score)
N1.Er.genes.3

N1.Er.genes.4 <- N1.c6.Er[[4]] %>% select(Term, Genes, Combined.Score)
N1.Er.genes.4


# other clusters - change the cluster number
N1.c6 <- ClusterMarkers %>% filter(cluster == 11 & avg_log2FC > 0)
genes <- N1.c6$gene

N1.c6.Er <- enrichr(genes, databases = db)
plotEnrich(N1.c6.Er[[1]], showTerms = 20, numChar = 40, y = "Count", orderBy = "P.value")
plotEnrich(N1.c6.Er[[2]], showTerms = 20, numChar = 40, y = "Count", orderBy = "P.value")
plotEnrich(N1.c6.Er[[3]], showTerms = 20, numChar = 40, y = "Count", orderBy = "P.value")
plotEnrich(N1.c6.Er[[4]], showTerms = 20, numChar = 40, y = "Count", orderBy = "P.value")

N1.Er.genes.1 <- N1.c6.Er[[1]] %>% select(Term, Genes, Combined.Score)
N1.Er.genes.1

N1.Er.genes.2 <- N1.c6.Er[[2]] %>% select(Term, Genes, Combined.Score)
N1.Er.genes.2

N1.Er.genes.3 <- N1.c6.Er[[3]] %>% select(Term, Genes, Combined.Score)
N1.Er.genes.3

N1.Er.genes.4 <- N1.c6.Er[[4]] %>% select(Term, Genes, Combined.Score)
N1.Er.genes.4


```

cluster 0 - astrocyte, radial glia, microglia, striatum - very few genes in these terms
cluster 1 - adrenal, thalmus, endothelial, astrocytes, neurons
clusters 2 - neural plate, stratum, neurons, NPC, stem, astro,neuroendocrine
cluster 3 - brain molecular layer, endothelial, astrocyte embryonic, 
cluster 4 - DG, striatum CA3, GABAergic neurons
cluster 5 - DG, neurons, Glutamatergic neurons
cluster 6 - neurons, astrocyte, microglia, GABAneurons
cluster 7 - neurons, glut and gaba
cluster 8 - astrocyte, endothelial, pericyte
cluster 9 - epithelial, embryonic astrocytes, GABA neurons
cluster 10 - epithelial
cluster 11 - endothelial, immune cells T cells


Expression of markers genes in Neurons2 

```{r}

feature_list = c("MKI67","SOX2","POU5F1","DLX2","PAX6","SOX9","HES1","NES","RBFOX3","MAP2","NCAM1","CD24","GRIA2","GRIN2B","GABBR1","GAD1","GAD2","GABRA1","GABRB2","TH","ALDH1A1","LMX1B","NR4A2","CORIN","CALB1","KCNJ6","CXCR4","ITGA6","SLC1A3","CD44","AQP4","S100B", "PDGFRA","OLIG2","MBP","CLDN11","VIM","VCAM1")

DoHeatmap(seu.q, features = feature_list, size=3, angle =90, group.bar.height = 0.02, group.by = 'RNA_snn_res.0.6')
DotPlot(seu.q, features = feature_list) +RotatedAxis()

PD_poulin = c("TH","SLC6A3","SLC18A2","SOX6","NDNF","SNCG","ALDH1A1","CALB1","TACR2","SLC17A6","SLC32A1","OTX2","GRP","LPL","CCK","VIP")

DoHeatmap(seu.q, features = PD_poulin, size=3, angle =90, group.bar.height = 0.02, group.by = 'RNA_snn_res.0.6')
DotPlot(seu.q, features = PD_poulin)+RotatedAxis()

ealryNeur = c("DCX","NEUROD1","TBR1")
proliferation = c("PCNA","MKI67")
neuralstem = c("SOX2","NES","PAX6","MASH1")

feature_list <- c("DCX","NEUROD1","TBR1","PCNA","MKI67","SOX2","NES","PAX6","MASH1")
DoHeatmap(seu.q, features = feature_list, size=3, angle =90, group.bar.height = 0.02, group.by = 'RNA_snn_res.0.6')
DotPlot(seu.q, features = feature_list)+RotatedAxis()
# no proliferation marker expression  PCNA or MKI67
# cluster 4 DA neurons - shows early neuron marker and low PAX 4
# cluster 3 has higher SOX2 - neuroblast marker / NPC marker

mat_neuron = c("RBFOX3","SYP","DLG45","VAMP1","VAMP2","TUBB3","SYT1","BSN","HOMER1","SLC17A6") 
# NeuN is FOX3 - RBFOX3
# PSD95 also SP-90 or DLG4
# VGLUT2 is SLC17A6
DoHeatmap(seu.q, features = mat_neuron, size=3, angle =90, group.bar.height = 0.02, group.by = 'RNA_snn_res.0.6')
# cluster 4 also show mature neuron markers
DotPlot(seu.q, features = mat_neuron)+RotatedAxis()
# excitatory neuron markers
ex = c("GRIA2","GRIA1","GRIA4","GRIN1","GRIN2B","GRIN2A","GRIN3A","GRIN3","GRIP1","CAMK2A")
DoHeatmap(seu.q, features = ex, size=3, angle =90, group.bar.height = 0.02, group.by = 'RNA_snn_res.0.6')
DotPlot(seu.q, features = ex)+RotatedAxis()
# inhibitory neuron markers
inh = c("GAD1","GAD2", "GAT1","PVALB","GABR2","GABR1","GBRR1","GABRB2","GABRB1","GABRB3","GABRA6","GABRA1","GABRA4","TRAK2")
DoHeatmap(seu.q, features = inh, size=3, angle =90, group.bar.height = 0.02, group.by = 'RNA_snn_res.0.6')
DotPlot(seu.q, features = inh)+RotatedAxis()
# cluster 4 is more excitatory than inhbitory but neither marker set has much expression 

### glia markers
microglia = c("PTPRC","AIF1","ADGRE1")  # ADGRE1 is a microglia marker F4/80, CD45 is PTPRC, gene name IBA1 is AIF1
astolgNPCpromicro = c("GFAP","S100B","SLC1A2","MBP","SOX10","SPP1","DCX","NEUROD1","TBR1","PCNA","MKI67","PTPRC","AIF1","ADGRE1")
# note GLT1 is EAAT2 which is SLC1A2 glutatmate transporter
# epithelial
epi = c("HES1","HES5","SOX2","SOX10","NES","CDH1","NOTCH1") # e-cadherin is CDH1

DoHeatmap(seu.q, features = astolgNPCpromicro, size=3, angle =90, group.bar.height = 0.02, group.by = 'RNA_snn_res.0.6')
DotPlot(seu.q, features = astolgNPCpromicro, group.by = 'RNA_snn_res.0.6')+RotatedAxis()
# cluster 4 is more excitatory than inhbitory but neither marker set has much expression 
DoHeatmap(seu.q, features = epi, size=3, angle =90, group.bar.height = 0.02, group.by = 'RNA_snn_res.0.6')
DotPlot(seu.q, features = epi, group.by = 'RNA_snn_res.0.6')+RotatedAxis()

# also add Radial glia marker overlap with Glia and Neurons

features <- c("PTPRC","AIF1","ADGRE1", "VIM", "TNC","PTPRZ1","FAM107A","HOPX","LIFR",
              "ITGB5","IL6ST")
DoHeatmap(seu.q, features = features, size=3, angle =90, group.bar.height = 0.02, group.by = 'RNA_snn_res.0.6')
DotPlot(seu.q, features = features, group.by = 'RNA_snn_res.0.6')+RotatedAxis()


```


Label the Neuron2 FACS population 

```{r}

Idents(seu.q) <- 'RNA_snn_res.0.6'
cluster.ids <- c("Neurons1","immatureNeurons1","Neurons2",
                 "Other","DAneurons1","DAneurons2","immatureNeurons2",
                 "DAneurons3","RG","immatureNeurons1","Epithelial","Endothelial")
unique(seu.q$RNA_snn_res.0.6)

names(cluster.ids) <- levels(seu.q)
seu.q <- RenameIdents(seu.q, cluster.ids)
seu.q$subgroups <- Idents(seu.q)

DimPlot(seu.q, reduction = "umap", label = TRUE, group.by = 'subgroups', repel = TRUE)


# label again with just numbering
# then I'll find markers in pairs to distinguish groups. 

Idents(seu.q) <- 'RNA_snn_res.0.6'
cluster.ids <- c("Neurons1","Neurons2","Neurons3",
                 "Other","DAneurons1","DAneurons2","Neurons4",
                 "DAneurons3","RG","Neurons2","Epithelial","Endothelial")
unique(seu.q$RNA_snn_res.0.6)

names(cluster.ids) <- levels(seu.q)
seu.q <- RenameIdents(seu.q, cluster.ids)
seu.q$number.groups <- Idents(seu.q)

DimPlot(seu.q, reduction = "umap", label = TRUE, group.by = 'number.groups', repel = TRUE)


saveRDS(seu.q, "/Users/rhalenathomas/Documents/Data/scRNAseq/PhenoID/scRNAseqSorted/objs/Neurons2LabelsSeu30092022.RDS")



```

Proportions of cell types

```{r}



```






Find markers in pairs to go back and classify the subgroups.
Will need to return to this for Neurons1 FACS

Neurons 5 and Neurons2 had similar markers and were merged
Subset again

```{r}

# faster to make a subset objects of only neurons and use find all markers

neuron.sub <- subset(seu.q, idents = c("Neurons1","Neurons2","Neurons3",
                                       "Neurons4"))

neuron.sub.markers <- FindAllMarkers(neuron.sub)

top5 <- neuron.sub.markers %>% group_by(cluster) %>% top_n(n=5, wt = avg_log2FC)
DoHeatmap(neuron.sub, features = top5$gene, size=3, angle =90, group.bar.height = 0.02)

DotPlot(neuron.sub, features = top5$gene) + RotatedAxis()


# neurons 5 was joined to Neurons 2
# cluster markers done again

# Neurons1 : MGP (targeting neural projections BMP signaling), HPD (excitatory and inibitory, could be cell adhesion or apoptosis), MSX1 (transcription factor BMP signaling, midbrain marker, developmental), CYP1B1 (redox homeostatis)   
# MGP, HPD, MSX1, CYP1B1

# there isn't a good proportion of cells expression any of the markers, Neurons2 has the best amount

# Neurons2: TFP12 serine protease melatonin conversion, PTN (cytokine signaling), LYgH (inhances nAChRs), S100A10 (modulates serotonin receptor), IFI27 (antiviral activity)
#TFP12,PTN, LY6H, S100A10, IFI27 

# Neurons3: SOX4, ASCL1 (neurogenesis),

# SOX4 is a marker of 3 but has high expression in 4 as well 

# Neurons4: PCAT4 (not noted in neurons), TPH1 (5HT synthesis), GK5 (neuronal maintainance), SST (somatostatin, GABA spike regulation), TTR (neural protective in AD)
# PCAT4, TPH1, GK5, SST, TTR

# markers to use

# Neurons1: MSX1, CYP1B1      
# Neurons2: LY6H, S100A10     
# Neurons3: SOX4, ASCL1 (Neurogenesis) Immature
# Neurons4: GK5, SST                         More mature



# Maybe neurons 1 and 2 could be merged

# lets see how the markers would look

neurons.1and2 <- FindMarkers(neuron.sub, ident.1 = c("Neurons1","Neurons2"),
                             ident.2 = c("Neurons3","Neurons4"))

top10 <- neurons.1and2 %>% top_n(n=10, wt = avg_log2FC)
ft.up <- rownames(top10) # up in Neurons1 and 3
top10 <- neurons.1and2 %>% top_n(n=-10, wt = avg_log2FC)
ft.down <- rownames(top10)
features <- c(ft.up,ft.down)

DoHeatmap(neuron.sub, features = features, size=3, angle =90, group.bar.height = 0.02)

DotPlot(neuron.sub, features = features) + RotatedAxis()

# all the markers were up regulated in neurons2 and not really neurons1
# I'll keep them separated


```


Use subgrouping and find cluster markers to look at neuronal subtypes.

```{r}

# faster to make a subset objects of only neurons and use find all markers

neuron.sub <- subset(seu.q, idents = c("DAneurons1","DAneurons2","DAneurons3"))

neuron.sub.markers <- FindAllMarkers(neuron.sub)

top5 <- neuron.sub.markers %>% group_by(cluster) %>% top_n(n=5, wt = avg_log2FC)
DoHeatmap(neuron.sub, features = top5$gene, size=3, angle =90, group.bar.height = 0.02)

DotPlot(neuron.sub, features = top5$gene) + RotatedAxis()

# markers are much clearer for the DA neuron subgroups

# DA neurons 1: WIF1, CYP1B1, IGFBP3, HPD, WFIKKN2
# WIF1 (secreted WNT inhibitor, promotes regeneration), CYP1B1 (redox homeostatis), IGFBP3 (prolactin secretion regulation hypothalmus), HPD (neuro protective), WFIKKN2 (Receptor for TNC)
# DA neurons2: CDH7, RUNX1T1, ASCL1, DLK1, MEG3
# CDH7 (neuro circuitry development, SEMA), RUNX1T1 (neuronal differentiation), ASCL1 (neuronal differentiation), DLK1 (neural differentation), MEG3 (neural homeostatis)  
# DA neurons3: PCAT4, NEUROD1, NCKAP5, GK5, SST
# PCAT4 (dendritic growth), NEUROD1 (neural differentation), NCKAP5 (Excitory neurons), GK5 (TH ), SST (regualates spike times)

# DA neurons1: CYP1B1, IGFBP3
# DA neurons2: RUNX1T1, ASCL1
# DA neurons3: NEUROD1, NCKAP5


```


Name the Neurons2 FACS population with the Neuron subtype latter.

```{r}

Idents(seu.q) <- 'RNA_snn_res.0.6'
cluster.ids <- c("Neurons-MSX1","Neurons-LY6H","Neurons-ASCL1",
                 "Other","DAneurons-CYP1B1","DAneurons-ASCL1","Neurons-GK5",
                 "DAneurons-NEUROD1","RG","Neurons-LY6H","Epithelial","Endothelial")

unique(seu.q$RNA_snn_res.0.6)

names(cluster.ids) <- levels(seu.q)
seu.q <- RenameIdents(seu.q, cluster.ids)
seu.q$cellsubgroups <- Idents(seu.q)

DimPlot(seu.q, reduction = "umap", label = TRUE, group.by = 'cellsubgroups', repel = TRUE)


```

```{r}

# save the Neurons2 with labels

saveRDS(seu.q, "/Users/rhalenathomas/Documents/Data/scRNAseq/PhenoID/scRNAseqSorted/objs/Neurons2LabelsSeu30092022.RDS")

```


FACS population Glia1 (should be astrocytes)

```{r}
# explore filtering
seu <- Glia1
seu
# 
VlnPlot(seu, pt.size = 0.10, features = c("nFeature_RNA", "nCount_RNA", "percent.mt"), ncol = 3)

VlnPlot(seu, pt.size = 0.10, features = c("nFeature_RNA"), y.max = 1000)
VlnPlot(seu, pt.size = 0.10, features = c("nFeature_RNA"), y.max = 500)
VlnPlot(seu, pt.size = 0.10, features = c("nCount_RNA"), y.max = 2000)

# filter more cells

seu.ft <- subset(seu, subset = nFeature_RNA > 300 & nCount_RNA > 500 & nCount_RNA < 10000) 
seu.ft

# still a lot of cells 47295
# will likely remove a lot more with the doublet finder


```

```{r}

saveRDS(seu.ft, "/Users/rhalenathomas/Documents/Data/scRNAseq/PhenoID/scRNAseqSorted/objs/Glia1AstroSeu01102022.RDS")

seu.ft <- readRDS("/Users/rhalenathomas/Documents/Data/scRNAseq/PhenoID/scRNAseqSorted/objs/Glia1AstroSeu01102022.RDS")

seu.ft <- readRDS("/Users/rhalenathomas/Documents/Data/scRNAseq/PhenoID/scRNAseqSorted/objs/Glia1AstroSeu01102022.RDS")

```

Doublet finder

```{r}

suppressMessages(require(DoubletFinder))

# filtering out MALAT1 and mitochondrial genes

seu.ft <- seu.ft[!grepl("MALAT1", rownames(seu.ft)), ]
seu.ft <- seu.ft[!grepl("^MT-", rownames(seu.ft)), ]

# like in the tutorial I'm following MALAT1 is the top most expressed gene.  The top genes are a lot of MT and Ribosomal genes

seu.ft[["percent.rb"]] <- PercentageFeatureSet(seu.ft, pattern = "^RP")

# down sample there are too many cells to run doublet finder
seu.sub <- subset(seu.ft, downsample = 20000)

seu.d = NormalizeData(seu.sub)
seu.d = FindVariableFeatures(seu.d, verbose = F)
seu.d = ScaleData(seu.d, vars.to.regress = c("nFeature_RNA", "percent.mt"),
    verbose = F)
seu.d = RunPCA(seu.d, verbose = F, npcs = 15)
seu.d = RunUMAP(seu.d, dims = 1:10, verbose = F)

nExp <- round(ncol(seu.d) * 0.15)  # expect more doublets because there is a lot more cells
seu.d <- doubletFinder_v3(seu.d, pN = 0.25, pK = 0.09, nExp = nExp, PCs = 1:10)
# the memory limit is reached here - I could run on compute canada
# For now I'll downsample
# this works

# name of the DF prediction can change, so extract the correct column name.
DF.name = colnames(seu.d@meta.data)[grepl("DF.classification", colnames(seu.d@meta.data))]


cowplot::plot_grid(ncol = 2, DimPlot(seu.d, group.by = "orig.ident") + NoAxes(),
    DimPlot(seu.d, group.by = DF.name) + NoAxes())

VlnPlot(seu.d, features = "nFeature_RNA", group.by = DF.name, pt.size = 0.1)

```




Remove the doublet cells

```{r}
seu.d <- seu.d[, seu.d@meta.data[, DF.name]== "Singlet"]
dim(seu.d)
dim(seu.sub)

# 20000 pre filter
# creates the expected percentage

```

Repeat workflow with doublet removed data and find clusters for 

```{r}
seu <- NormalizeData(seu.d, normalization.method = "LogNormalize", scale.factor = 10000)
seu <- FindVariableFeatures(seu, selection.method = "vst", nfeatures = 2000)
seu <- ScaleData(seu)
seu <- RunPCA(seu)
seu <- RunUMAP(seu, reduction = "pca", n.neighbors = 43, dims = 1:30)
DimPlot(seu, reduction = "umap")

seu.q <- FindNeighbors(seu, dims = 1:25, k.param = 43)
seu.q <- FindClusters(seu.q, resolution = c(0,0.2,0.4,0.6))
seu.q <- FindClusters(seu.q, resolution = c(0,0.05,0.1,0.8))
library(clustree)
clustree(seu.q)
DimPlot(seu.q, reduction = "umap", group.by = 'RNA_snn_res.0.05')
DimPlot(seu.q, reduction = "umap", group.by = 'RNA_snn_res.0.1')
DimPlot(seu.q, reduction = "umap", group.by = 'RNA_snn_res.0.2')
DimPlot(seu.q, reduction = "umap", group.by = 'RNA_snn_res.0.4')
DimPlot(seu.q, reduction = "umap", group.by = 'RNA_snn_res.0.6')
DimPlot(seu.q, reduction = "umap", group.by = 'RNA_snn_res.0.8')




```
Look at some expression markers in a feature plot

```{r}
# genes reported up in Astrocytes
FeaturePlot(seu.q, features = c("GFAP","S100B","AQP4","SLC1A3","GJA1",
                                "APOE","TEAD1","GSTA4","SOX9",
                                "VIM","HMG20A","ALDH1L1"))
# almost no GFAP expression and lots of S100B everywhere

```



Predict cell types

```{r}


# SNCA and control midbrain organoids 165 days in culture
MBO <- readRDS("/Users/rhalenathomas/Documents/Data/scRNAseq/AST23_BrainComm/MBOclusters_names29072021.rds")

# Midbrain  AIW002 120 days in culture
AIWMBO <- readRDS("/Users/rhalenathomas/Documents/Data/scRNAseq/AIWtrio120days/MOintegratedClusterK123res0.8.names_nov16_2021")

# Midbrain AIW002 60 days in culture

AIW60 <- readRDS("/Users/rhalenathomas/Documents/Data/scRNAseq/AIWtrio60days/AWI002ParkinKOPinkKO60days_labels_14052022.rds")


#first predict with the MBO data
Idents(MBO) <- "cluster_labels"
DefaultAssay(MBO) <- "RNA"

# find the reference anchors
print("finding reference anchors")
anchors <- FindTransferAnchors(reference = MBO ,query = seu.q, dims = 1:25)
print("getting predictions")
predictions <- TransferData(anchorset = anchors, refdata = MBO$cluster_labels)
seu.q <- AddMetaData(seu.q, metadata = predictions)
print(table(seu.q$predicted.id))

Idents(seu.q) <- 'predicted.id'
# add new dataslot for MBO predicted ID to make the next prediction
seu.q$MBOAST23.pred <- Idents(seu.q)
DimPlot(seu.q, group.by = 'MBOAST23.pred', label = TRUE)
 
## check the proportion of cell types predicted in each cluster
t.lables <- as.data.frame(table(seu.q$RNA_snn_res.0.8, seu.q$MBOAST23.pred))
t.lables$Freq <- as.double(t.lables$Freq)

# try bar chart
ggplot(t.lables, aes(y = Freq, x = Var1, fill = Var2)) + geom_bar(position = "stack", stat= "identity")

# clusters don't break up by the predicted cell types

############ another predictions now using the AIW organoids

Idents(AIWMBO) <- "res08names"
DefaultAssay(AIWMBO) <- "RNA"

anchors <- FindTransferAnchors(reference = AIWMBO ,query = seu.q, dims = 1:25)
print("getting predictions")
predictions <- TransferData(anchorset = anchors, refdata = AIWMBO$res08names)
seu.q <- AddMetaData(seu.q, metadata = predictions)
print(table(seu.q$predicted.id))

Idents(seu.q) <- 'predicted.id'
# add new dataslot for MBO predicted ID to make the next prediction
seu.q$MBOAIW.pred <- Idents(seu.q)
DimPlot(seu.q, group.by = 'MBOAIW.pred', label = TRUE)
 
## check the proportion of cell types predicted in each cluster
t.lables <- as.data.frame(table(seu.q$RNA_snn_res.0.8, seu.q$MBOAIW.pred))
t.lables$Freq <- as.double(t.lables$Freq)

# try bar chart
ggplot(t.lables, aes(y = Freq, x = Var1, fill = Var2)) + geom_bar(position = "stack", stat= "identity")

# the predicted cell types make more sense from the AIW002 organoid
# now predict with the AIW002 60 days organoid

Idents(AIW60) <- "cluster.ids"
DefaultAssay(AIW60) <- "RNA"

anchors <- FindTransferAnchors(reference = AIW60, query = seu.q, dims = 1:25)
print("getting predictions")
predictions <- TransferData(anchorset = anchors, refdata = AIW60$cluster.ids) 
seu.q <- AddMetaData(seu.q, metadata = predictions)
print(table(seu.q$predicted.id))

Idents(seu.q) <- 'predicted.id'
# add new dataslot for MBO predicted ID to make the next prediction
seu.q$AIW60.pred <- Idents(seu.q)
DimPlot(seu.q, group.by = 'AIW60.pred', label = TRUE)
 
## check the proportion of cell types predicted in each cluster
t.lables <- as.data.frame(table(seu.q$RNA_snn_res.0.8, seu.q$AIW60.pred))
t.lables$Freq <- as.double(t.lables$Freq)

# try bar chart
ggplot(t.lables, aes(y = Freq, x = Var1, fill = Var2)) + geom_bar(position = "stack", stat= "identity")

# save ojbect with predicitons
saveRDS(seu.q, "/Users/rhalenathomas/Documents/Data/scRNAseq/PhenoID/scRNAseqSorted/objs/Glia1PredictionsSeu01102022.RDS")



```


Look at the reference objects 

```{r}

# triplication
DimPlot(MBO) # has two groups of astrocytes
# AIW 60 days
DimPlot(AIW60) # has one group of astrocytes
# AIW 120 days
DimPlot(AIWMBO) # has two astrocyte groups


```

Look more at the predictions

```{r}

# AIW002 120 days predictions
t.lables <- as.data.frame(table(seu.q$RNA_snn_res.0.2, seu.q$MBOAIW.pred))
t.lables$Freq <- as.double(t.lables$Freq)
ggplot(t.lables, aes(y = Freq, x = Var1, fill = Var2)) + geom_bar(position = "stack", stat= "identity") + RotatedAxis() 
top.pred.celltype.AIW120 <- as.data.frame(t.lables  %>% group_by(Var1)  %>% top_n(2, Freq))
df.top.aiw120 <- top.pred.celltype.AIW120[order(top.pred.celltype.AIW120$Var1,-top.pred.celltype.AIW120$Freq),]
row.names(df.top.aiw120) <- NULL
df.top.aiw120$I <- row.names(df.top.aiw120)

# AIW002 60 days predictions
t.lables <- as.data.frame(table(seu.q$RNA_snn_res.0.2, seu.q$AIW60.pred))
t.lables$Freq <- as.double(t.lables$Freq)
ggplot(t.lables, aes(y = Freq, x = Var1, fill = Var2)) + geom_bar(position = "stack", stat= "identity") + RotatedAxis() 
top.pred.celltype.AIW60 <-as.data.frame(t.lables  %>% group_by(Var1)  %>% top_n(2, Freq))
df.top.aiw60 <- top.pred.celltype.AIW60[order(top.pred.celltype.AIW60$Var1,-top.pred.celltype.AIW60$Freq),]
row.names(df.top.aiw60) <- NULL
df.top.aiw60$I <- row.names(df.top.aiw60)


# AST23 165 days predictions
t.lables <- as.data.frame(table(seu.q$RNA_snn_res.0.2, seu.q$MBOAST23.pred))
t.lables$Freq <- as.double(t.lables$Freq)
ggplot(t.lables, aes(y = Freq, x = Var1, fill = Var2)) + geom_bar(position = "stack", stat= "identity") + RotatedAxis() 
top.pred.celltype.AST23 <- as.data.frame(t.lables  %>% group_by(Var1)  %>% top_n(2, Freq))
df.top.AST23 <- top.pred.celltype.AST23[order(top.pred.celltype.AST23$Var1,-top.pred.celltype.AST23$Freq),]
row.names(df.top.AST23) <- NULL
df.top.AST23$I <- row.names(df.top.AST23)

pred.table <- merge(df.top.AST23, df.top.aiw60, by = 'I', all = TRUE)
pred.table <- merge(pred.table, df.top.aiw120, by = 'I')
pred.table

```
These predictions are not good.  There are several astrocyte markers by expression levels.  Everything is predicted as Radial glia or oligo dendrocytes


Try to predict with the astrocyte Kamath data

```{r}

astro.ref <- readRDS("/Users/rhalenathomas/Documents/Data/scRNAseq/Macosko_Data/PD_astro.Rds")
# need to make PCA and UMAP
astro.ref <- NormalizeData(astro.ref)
astro.ref <- FindVariableFeatures(astro.ref, selection.method = "vst", nfeatures = 2000)
astro.ref <- ScaleData(astro.ref)
astro.ref <- RunPCA(astro.ref)
astro.ref <- RunUMAP(astro.ref, reduction = "pca", n.neighbors = 205, dims = 1:25)

colnames(astro.ref@meta.data)


Idents(astro.ref) <- "Cell_Subtype"
DefaultAssay(astro.ref) <- "RNA"

# find the reference anchors
print("finding reference anchors")
anchors <- FindTransferAnchors(reference = astro.ref ,query = seu.q, dims = 1:20)
print("getting predictions")
predictions <- TransferData(anchorset = anchors, refdata = astro.ref$Cell_Subtype, k.weight = 10)
seu.q <- AddMetaData(seu.q, metadata = predictions)
print(table(seu.q$predicted.id))

Idents(seu.q) <- 'predicted.id'
# add new dataslot for MBO predicted ID to make the next prediction
seu.q$astro.pred <- Idents(seu.q)
DimPlot(seu.q, group.by = 'astro.pred', label = TRUE)
table(seu.q$astro.pred)

seu.q$predicted.id <- ifelse(seu.q$prediction.score.max > 0.95, seu.q$predicted.id, NA)
print(table(seu.q$predicted.id))

Idents(seu.q) <- 'predicted.id'
seu.q$astro.pred.thresh <- Idents(seu.q)
DimPlot(seu.q, group.by = 'astro.pred.thresh', label = TRUE)
table(seu.q$astro.pred.thresh)

# 19986 Astro_VIM_TNFSRF12A no threshold      Astro_GLYATL2 14
# 8376 Astro_VIM_TNFSRF12A   with 95% threshold


t.lables <- as.data.frame(table(seu.q$RNA_snn_res.0.2, seu.q$astro.pred.thresh))
t.lables$Freq <- as.double(t.lables$Freq)
ggplot(t.lables, aes(y = Freq, x = Var1, fill = Var2)) + geom_bar(position = "stack", stat= "identity") + RotatedAxis() 
top.pred.astro <- as.data.frame(t.lables  %>% group_by(Var1)  %>% top_n(2, Freq))
df.top.astro <- top.pred.astro[order(top.pred.astro$Var1,-top.pred.astro$Freq),]
row.names(df.top.astro) <- NULL


t.lables <- as.data.frame(table(seu.q$RNA_snn_res.0.2, seu.q$astro.pred))
t.lables$Freq <- as.double(t.lables$Freq)
ggplot(t.lables, aes(y = Freq, x = Var1, fill = Var2)) + geom_bar(position = "stack", stat= "identity") + RotatedAxis() 
top.pred.astro <- as.data.frame(t.lables  %>% group_by(Var1)  %>% top_n(2, Freq))
df.top.astro <- top.pred.astro[order(top.pred.astro$Var1,-top.pred.astro$Freq),]
row.names(df.top.astro) <- NULL



```





Look at cluster markers

```{r}

Idents(seu.q) <- 'RNA_snn_res.0.2'
ClusterMarkers <- FindAllMarkers(seu.q, only.pos = TRUE)

top5 <- ClusterMarkers %>% group_by(cluster) %>% top_n(n=5, wt = avg_log2FC)
DoHeatmap(seu.q, features = top5$gene, size=3, angle =90, group.bar.height = 0.02)

write.csv(ClusterMarkers,"/Users/rhalenathomas/Documents/Data/scRNAseq/PhenoID/scRNAseqSorted/Glia1AstrocytesClusterMarkers_new.csv")

# for the res 0.6 the largers groups 0 and 1 don't have great markers and none of the markers are really very good.  
# I'll rerun with res 0.2
unique(seu.q$RNA_snn_res.0.2)

# still not much better



```



Check cell type markers with EnrichR

```{r}

library(enrichR)

setEnrichrSite("Enrichr") # Human genes
# list of all the databases

# libaries with cell types

db <- c('Allen_Brain_Atlas_up','Descartes_Cell_Types_and_Tissue_2021',
        'CellMarker_Augmented_2021','Azimuth_Cell_Types_2021')

# enrichr(genes, databases = NULL)

#I'll run the clusters one at a time

N1.c0 <- ClusterMarkers %>% filter(cluster == 5 & avg_log2FC > 0)
genes <- N1.c0$gene

N1.c0.Er <- enrichr(genes, databases = db)
plotEnrich(N1.c0.Er[[1]], showTerms = 20, numChar = 40, y = "Count", orderBy = "P.value")
plotEnrich(N1.c0.Er[[2]], showTerms = 20, numChar = 40, y = "Count", orderBy = "P.value")
plotEnrich(N1.c0.Er[[3]], showTerms = 20, numChar = 40, y = "Count", orderBy = "P.value")
plotEnrich(N1.c0.Er[[4]], showTerms = 20, numChar = 40, y = "Count", orderBy = "P.value")

N1.Er.genes.1 <- N1.c0.Er[[1]] %>% select(Term, Genes, Combined.Score)
N1.Er.genes.1

N1.Er.genes.2 <- N1.c0.Er[[2]] %>% select(Term, Genes, Combined.Score)
N1.Er.genes.2

N1.Er.genes.3 <- N1.c0.Er[[3]] %>% select(Term, Genes, Combined.Score)
N1.Er.genes.3


N1.Er.genes.4 <- N1.c0.Er[[4]] %>% select(Term, Genes, Combined.Score)
N1.Er.genes.4



# cluster 0 - Cell type marker library - Brain astrocyte top hit and embryonic astrocytes
# gene list in term: brain astrocyte	EFEMP1;NFIA;LIX1;PSAP;KIF21A;S100B;CRYAB;DKK3
# embryo astrocytes	SOX2;BEX1;HMGCS1;PTPRZ1;LIX1;S100B;DKK3;ITM2C

# cluster 1 - stem cell pericyte (brain), stelate, astrocyte

# cluster 2 - hypothalmus, endothelial cells, macrophage
# endothelial CSTB;PRELID1;MT1X;CRIP2;RHOC;TMEM141;MT2A;RPS28;CCDC85B;EIF3I;RBP1;ID1;C4ORF3;ID3;PCBD1;MSX1;PPIC

# cluster 3 - smooth muscle cells

# cluster 4 - NK cells, fibroblasts
# NK cells ITGB1;RAB5C;GSTP1;PDCD5;EEF1B2;TGOLN2;SDCBP;MT2A;LDHA;SNRPD2;YWHAQ;ZNF326;TMSB10;CCDC50
# fibroblasts 	COL3A1;CALD1;COL6A3

# cluster 5 - endothelial cells, NK cells, CD8+

# cluster 6 - stromal cells eurythroblasts, none-neuronal, oligo

# reran and now there are only 5 clusters
# repeat checking


```

Cluster 0 - astrocytes
Cluster 1 - pericyte astrocyte (weak still)
Cluster 2 - endothelial
Cluster 3 - smooth muscle
Cluster 4 - NK/fibroblast
Cluster 5 - endothelial
Cluster 6 - non- neuronal







```{r}

VlnPlot(seu.q, features = c("CD44","ITGB1","S100B"), group.by = 'orig.ident')
VlnPlot(seu.ft, features = c("CD44","ITGB1","S100B"), group.by = 'orig.ident')

```

Check expression of known markers

```{r}

Idents(seu.q) <- 'RNA_snn_res.0.2'

feature_list = c("MKI67","SOX2","POU5F1","DLX2","PAX6","SOX9","HES1","NES","RBFOX3","MAP2","NCAM1","CD24","GRIA2","GRIN2B","GABBR1","GAD1","GAD2","GABRA1","GABRB2","TH","ALDH1A1","LMX1B","NR4A2","CORIN","CALB1","KCNJ6","CXCR4","ITGA6","SLC1A3","CD44","AQP4","S100B", "PDGFRA","OLIG2","MBP","CLDN11","VIM","VCAM1")

DoHeatmap(seu.q, features = feature_list, size=3, angle =90, group.bar.height = 0.02)
DotPlot(seu.q, features = feature_list) +RotatedAxis()

PD_poulin = c("TH","SLC6A3","SLC18A2","SOX6","NDNF","SNCG","ALDH1A1","CALB1","TACR2","SLC17A6","SLC32A1","OTX2","GRP","LPL","CCK","VIP")

DoHeatmap(seu.q, features = PD_poulin, size=3, angle =90, group.bar.height = 0.02)
DotPlot(seu.q, features = PD_poulin)+RotatedAxis()

ealryNeur = c("DCX","NEUROD1","TBR1")
proliferation = c("PCNA","MKI67")
neuralstem = c("SOX2","NES","PAX6","MASH1")

feature_list <- c("DCX","NEUROD1","TBR1","PCNA","MKI67","SOX2","NES","PAX6","MASH1")
DoHeatmap(seu.q, features = feature_list, size=3, angle =90, group.bar.height = 0.02)
DotPlot(seu.q, features = feature_list)+RotatedAxis()


mat_neuron = c("RBFOX3","SYP","DLG45","VAMP1","VAMP2","TUBB3","SYT1","BSN","HOMER1","SLC17A6") 
# NeuN is FOX3 - RBFOX3
# PSD95 also SP-90 or DLG4
# VGLUT2 is SLC17A6
DoHeatmap(seu.q, features = mat_neuron, size=3, angle =90, group.bar.height = 0.02)
# cluster 4 also show mature neuron markers
DotPlot(seu.q, features = mat_neuron)+RotatedAxis()
# excitatory neuron markers
ex = c("GRIA2","GRIA1","GRIA4","GRIN1","GRIN2B","GRIN2A","GRIN3A","GRIN3","GRIP1","CAMK2A")
DoHeatmap(seu.q, features = ex, size=3, angle =90, group.bar.height = 0.02)
DotPlot(seu.q, features = ex)+RotatedAxis()
# inhibitory neuron markers
inh = c("GAD1","GAD2", "GAT1","PVALB","GABR2","GABR1","GBRR1","GABRB2","GABRB1","GABRB3","GABRA6","GABRA1","GABRA4","TRAK2")
DoHeatmap(seu.q, features = inh, size=3, angle =90, group.bar.height = 0.02)
DotPlot(seu.q, features = inh)+RotatedAxis()
# cluster 4 is more excitatory than inhbitory but neither marker set has much expression 

### glia markers
microglia = c("PTPRC","AIF1","ADGRE1")  # ADGRE1 is a microglia marker F4/80, CD45 is PTPRC, gene name IBA1 is AIF1
astolgNPCpromicro = c("GFAP","S100B","SLC1A2","MBP","SOX10","SPP1","DCX","NEUROD1","TBR1","PCNA","MKI67","PTPRC","AIF1","ADGRE1")
# note GLT1 is EAAT2 which is SLC1A2 glutatmate transporter
# epithelial
epi = c("HES1","HES5","SOX2","SOX10","NES","CDH1","NOTCH1") # e-cadherin is CDH1

DoHeatmap(seu.q, features = astolgNPCpromicro, size=3, angle =90, group.bar.height = 0.02)
DotPlot(seu.q, features = astolgNPCpromicro)+RotatedAxis()
# cluster 4 is more excitatory than inhbitory but neither marker set has much expression 
DoHeatmap(seu.q, features = epi, size=3, angle =90, group.bar.height = 0.02)
DotPlot(seu.q, features = epi)+RotatedAxis()

# also add Radial glia marker overlap with Glia and Neurons

features <- c("PTPRC","AIF1","ADGRE1", "VIM", "TNC","PTPRZ1","FAM107A","HOPX","LIFR",
              "ITGB5","IL6ST")
DoHeatmap(seu.q, features = features, size=3, angle =90, group.bar.height = 0.02)
DotPlot(seu.q, features = features)+RotatedAxis()




```

No TH expression

clusters 5 VIM highest, S100 B 
Cluster 4 
Cluster 3 has some cells with high OTX2, NES indicates NPC/Precursors
Cluster 2 has some SOX2 and PAX6 indicates NPC, VAMP2 indicating neurons, S100B highest and most - indicates astrocytes, also MBP indicates oligos
Cluster 1
Cluster 0 


Lable the clusters 

```{r}

Idents(seu.q) <- 'RNA_snn_res.0.2'

#seu.q <- BuildClusterTree(seu.q, reorder = TRUE, reorder.numeric = TRUE)
unique(seu.q$RNA_snn_res.0.2)

cluster.ids <- c("Astrocytes1","Astrocytes2","Precursors","RG1","RG2","Endothelial")

names(cluster.ids) <- levels(seu.q)
seu.q <- RenameIdents(seu.q, cluster.ids)
seu.q$subgroups <- Idents(seu.q)

#DimPlot(seu.q, group.by = 'RNA_snn_res.0.2', label = TRUE)
DimPlot(seu.q, reduction = "umap", label = TRUE, group.by = 'subgroups', repel = TRUE)

# something weird is going on in the order

#saveRDS(seu.q, "/Users/rhalenathomas/Documents/Data/scRNAseq/PhenoID/scRNAseqSorted/objs/Glia1LabledSeu301102022.RDS")


```



Compare the Astrocyte groups and get some markers for sub groups

```{r}


astro.sub.markers <- FindMarkers(seu.q, ident.1 = "Astrocytes1", ident.2 = "Astrocytes2", only.pos = FALSE)
#top5 <- astro.sub.markers %>% top_n(n=5, wt = avg_log2FC)

DoHeatmap(seu.q, features = c("PLCG2","PTPRZ1","SNHG25","VCAN","LUM","DCN","S1004A"), size=3, angle =90, group.bar.height = 0.02)

astro2 <- rownames(astro.sub.markers %>% filter(avg_log2FC < 0.05))

DotPlot(seu.q, features = c("PLCG2","PTPRZ1","SNHG25","VCAN","LUM","DCN","S1004A")) + RotatedAxis()

DoHeatmap(seu.q, features = astro2[1:15], size=3, angle =90, group.bar.height = 0.02)
DotPlot(seu.q, features = astro2[1:15]) +RotatedAxis()


# radial glia subtyping
Idents(seu.q) <- ('subgroups')
rg.sub.markers <- FindMarkers(seu.q, ident.1 = "RG1", ident.2 = "RG2", only.pos = FALSE)
top5.up <- rg.sub.markers %>% top_n(n=10, wt = avg_log2FC)
top5.down <- rg.sub.markers %>% top_n(n=-10, wt = avg_log2FC)
ft <- rownames(top5.up)

DoHeatmap(seu.q, features = ft, size=3, angle =90, group.bar.height = 0.02)
DotPlot(seu.q, features = ft) + RotatedAxis()


ft <- rownames(top5.down)

DoHeatmap(seu.q, features = ft, size=3, angle =90, group.bar.height = 0.02)
DotPlot(seu.q, features = ft) + RotatedAxis()

npc.markers <- FindMarkers(seu.q, ident.1 = "Precursors", ident.2 = c("Astrocytes2","Astrocytes1"), only.pos = FALSE)
top10.npc <- npc.markers %>% top_n(n=10, wt = avg_log2FC)
npc.markers <- npc.markers %>% filter(avg_log2FC > 0)
dim(npc.markers)

ft <- rownames(top10.npc)
# consider naming precursors as astrocytes3
DoHeatmap(seu.q, features = ft, size=3, angle =90, group.bar.height = 0.02)
DotPlot(seu.q, features = ft) + RotatedAxis()


npc.markers.rg <- FindMarkers(seu.q, ident.1 = "Precursors", ident.2 = c("RG2","RG1"), only.pos = TRUE)
top10.npc <- npc.markers.rg %>% top_n(n=10, wt = avg_log2FC)

ft <- rownames(top10.npc)
# consider naming precursors as astrocytes3
DoHeatmap(seu.q, features = ft, size=3, angle =90, group.bar.height = 0.02)
DotPlot(seu.q, features = ft) + RotatedAxis()

## these have more differences from RG than Astrocytes


```

Add subtype gene ids

```{r}

DimPlot(seu.q, group.by = 'RNA_snn_res.0.2')

cluster.ids <- c("Astrocytes-PLCG2","Astrocytes-DNC","Astrocytes-IGFBP2",
                 "RG1-CDKN1C","RG2-TYRP1","Endothelial")
#cluster.ids <- c("Astrocytes1","Astrocytes2","Precursors(Astrocytes)","RG1","RG2","Endothelial")

names(cluster.ids) <- levels(seu.q)
seu.q <- RenameIdents(seu.q, cluster.ids)
seu.q$Cell_types <- Idents(seu.q)

DimPlot(seu.q, reduction = "umap", label = TRUE, group.by = 'Cell_types', repel = TRUE)


saveRDS(seu.q, "/Users/rhalenathomas/Documents/Data/scRNAseq/PhenoID/scRNAseqSorted/objs/Glia1LabledSeu301102022.RDS")

# label with main cell type groups 

DimPlot(seu.q, group.by = 'RNA_snn_res.0.2')

cluster.ids <- c("Astrocytes","Astrocytes","Astrocytes",
                 "RG","RG","Endothelial")
#cluster.ids <- c("Astrocytes1","Astrocytes2","Precursors(Astrocytes)","RG1","RG2","Endothelial")

names(cluster.ids) <- levels(seu.q)
seu.q <- RenameIdents(seu.q, cluster.ids)
seu.q$Cell_Type <- Idents(seu.q)

DimPlot(seu.q, reduction = "umap", label = TRUE, group.by = 'Cell_Type', repel = TRUE)


saveRDS(seu.q, "/Users/rhalenathomas/Documents/Data/scRNAseq/PhenoID/scRNAseqSorted/objs/Glia1LabledSeu301102022.RDS")




```


Proportions of cells types

```{r}



```


Quick check the Glial2 

```{r}

# explore filtering
seu <- Glia2
seu
# 
VlnPlot(seu, pt.size = 0.10, features = c("nFeature_RNA", "nCount_RNA", "percent.mt"), ncol = 3)

VlnPlot(seu, pt.size = 0.10, features = c("nFeature_RNA"), y.max = 1000)
VlnPlot(seu, pt.size = 0.10, features = c("nFeature_RNA"), y.max = 500)
VlnPlot(seu, pt.size = 0.10, features = c("nCount_RNA"), y.max = 2000)

# filter more cells

seu.ft <- subset(seu, subset = nFeature_RNA > 250 & nCount_RNA > 250 & nCount_RNA < 10000) 
seu.ft

VlnPlot(seu.ft, pt.size = 0.10, features = c("nFeature_RNA"), y.max = 2000)

```
```{r}

VlnPlot(seu.ft.glia, features = c("CD44","S100B","ITGB1"))
VlnPlot(seu.ft, features = c("CD44","S100B","ITGB1"), group.by = 'orig.ident')
# both glia populations are similar


```

Levels seem similar in Glia1 and Glia2

Remove doublets and start to process Glia 2

```{r}

suppressMessages(require(DoubletFinder))

# filtering out MALAT1 and mitochondrial genes

seu.ft <- seu.ft[!grepl("MALAT1", rownames(seu.ft)), ]
seu.ft <- seu.ft[!grepl("^MT-", rownames(seu.ft)), ]

# like in the tutorial I'm following MALAT1 is the top most expressed gene.  The top genes are a lot of MT and Ribosomal genes

seu.ft[["percent.rb"]] <- PercentageFeatureSet(seu.ft, pattern = "^RP")

seu.d = NormalizeData(seu.ft)
seu.d = FindVariableFeatures(seu.d, verbose = F)
seu.d = ScaleData(seu.d, vars.to.regress = c("nFeature_RNA", "percent.mt"),
    verbose = F)
seu.d = RunPCA(seu.d, verbose = F, npcs = 15)
seu.d = RunUMAP(seu.d, dims = 1:10, verbose = F)

nExp <- round(ncol(seu.d) * 0.08)  # expect more doublets because there is a lot more cells
seu.d <- doubletFinder_v3(seu.d, pN = 0.25, pK = 0.09, nExp = nExp, PCs = 1:10)
# the memory limit is reached here - I could run on compute canada
# For now I'll downsample
# this works

# name of the DF prediction can change, so extract the correct column name.
DF.name = colnames(seu.d@meta.data)[grepl("DF.classification", colnames(seu.d@meta.data))]


cowplot::plot_grid(ncol = 2, DimPlot(seu.d, group.by = "orig.ident") + NoAxes(),
    DimPlot(seu.d, group.by = DF.name) + NoAxes())

VlnPlot(seu.d, features = "nFeature_RNA", group.by = DF.name, pt.size = 0.1)

```


```{r}

seu.d <- seu.d[, seu.d@meta.data[, DF.name]== "Singlet"]
dim(seu.d)
dim(seu.ft)



```

Cluster 

```{r}
seu <- NormalizeData(seu.d, normalization.method = "LogNormalize", scale.factor = 10000)
seu <- FindVariableFeatures(seu, selection.method = "vst", nfeatures = 2000)
seu <- ScaleData(seu)
seu <- RunPCA(seu)
seu <- RunUMAP(seu, reduction = "pca", n.neighbors = 25, dims = 1:30)
DimPlot(seu, reduction = "umap")

seu.q <- FindNeighbors(seu, dims = 1:25, k.param = 25)
seu.q <- FindClusters(seu.q, resolution = c(0,0.05,0.2,0.4,0.5,0.6,0.8))
library(clustree)

DimPlot(seu.q, reduction = "umap", group.by = 'RNA_snn_res.0.05')
DimPlot(seu.q, reduction = "umap", group.by = 'RNA_snn_res.0.1')
DimPlot(seu.q, reduction = "umap", group.by = 'RNA_snn_res.0.2')
DimPlot(seu.q, reduction = "umap", group.by = 'RNA_snn_res.0.4')
DimPlot(seu.q, reduction = "umap", group.by = 'RNA_snn_res.0.6')
DimPlot(seu.q, reduction = "umap", group.by = 'RNA_snn_res.0.8')
clustree(seu.q)
# 0.4 is likely the best annotate subgroups


```

Predict cell types 


```{r}

# SNCA and control midbrain organoids 165 days in culture
MBO <- readRDS("/Users/rhalenathomas/Documents/Data/scRNAseq/AST23_BrainComm/MBOclusters_names29072021.rds")

# Midbrain  AIW002 120 days in culture
AIWMBO <- readRDS("/Users/rhalenathomas/Documents/Data/scRNAseq/AIWtrio120days/MOintegratedClusterK123res0.8.names_nov16_2021")

# Midbrain AIW002 60 days in culture

AIW60 <- readRDS("/Users/rhalenathomas/Documents/Data/scRNAseq/AIWtrio60days/AWI002ParkinKOPinkKO60days_labels_14052022.rds")


#first predict with the MBO data
Idents(MBO) <- "cluster_labels"
DefaultAssay(MBO) <- "RNA"

# find the reference anchors
print("finding reference anchors")
anchors <- FindTransferAnchors(reference = MBO ,query = seu.q, dims = 1:25)
print("getting predictions")
predictions <- TransferData(anchorset = anchors, refdata = MBO$cluster_labels)
seu.q <- AddMetaData(seu.q, metadata = predictions)

Idents(seu.q) <- 'predicted.id'
# add new dataslot for MBO predicted ID to make the next prediction
seu.q$MBOAST23.pred <- Idents(seu.q)
DimPlot(seu.q, group.by = 'MBOAST23.pred', label = TRUE)

# see how accurate the predictions are
seu.q$predicted.id <- ifelse(seu.q$prediction.score.max > 0.95, seu.q$predicted.id, "None")

Idents(seu.q) <- 'predicted.id'
seu.q$MBOAST23.thresh <- Idents(seu.q)
DimPlot(seu.q, group.by = 'predicted.id', label = TRUE)
table(seu.q$MBOAST23.pred)
table(seu.q$MBOAST23.thresh)


 
## check the proportion of cell types predicted in each cluster
t.lables <- as.data.frame(table(seu.q$RNA_snn_res.0.4, seu.q$MBOAST23.pred))
t.lables$Freq <- as.double(t.lables$Freq)

# try bar chart
ggplot(t.lables, aes(y = Freq, x = Var1, fill = Var2)) + geom_bar(position = "stack", stat= "identity")

# clusters don't break up by the predicted cell types

############ another predictions now using the AIW organoids

Idents(AIWMBO) <- "res08names"
DefaultAssay(AIWMBO) <- "RNA"

anchors <- FindTransferAnchors(reference = AIWMBO ,query = seu.q, dims = 1:25)
print("getting predictions")
predictions <- TransferData(anchorset = anchors, refdata = AIWMBO$res08names)
seu.q <- AddMetaData(seu.q, metadata = predictions)
print(table(seu.q$predicted.id))

Idents(seu.q) <- 'predicted.id'
# add new dataslot for MBO predicted ID to make the next prediction
seu.q$AIW120.pred <- Idents(seu.q)
DimPlot(seu.q, group.by = 'AIW120.pred', label = TRUE)
 
## check the proportion of cell types predicted in each cluster
t.lables <- as.data.frame(table(seu.q$RNA_snn_res.0.4, seu.q$MBOAIW.pred))
t.lables$Freq <- as.double(t.lables$Freq)
# try bar chart
ggplot(t.lables, aes(y = Freq, x = Var1, fill = Var2)) + geom_bar(position = "stack", stat= "identity")

# see how accurate the predictions are
seu.q$predicted.id <- ifelse(seu.q$prediction.score.max > 0.95, seu.q$predicted.id, "None")

Idents(seu.q) <- 'predicted.id'
seu.q$AIW120.thresh <- Idents(seu.q)
DimPlot(seu.q, group.by = 'AIW120.thresh', label = TRUE)
table(seu.q$AIW120.pred)
table(seu.q$AIW120.thresh)

# the predicted cell types make more sense from the AIW002 organoid
# now predict with the AIW002 60 days organoid

Idents(AIW60) <- "cluster.ids"
DefaultAssay(AIW60) <- "RNA"

anchors <- FindTransferAnchors(reference = AIW60, query = seu.q, dims = 1:25)
print("getting predictions")
predictions <- TransferData(anchorset = anchors, refdata = AIW60$cluster.ids) 
seu.q <- AddMetaData(seu.q, metadata = predictions)
print(table(seu.q$predicted.id))

Idents(seu.q) <- 'predicted.id'
# add new dataslot for MBO predicted ID to make the next prediction
seu.q$AIW60.pred <- Idents(seu.q)
DimPlot(seu.q, group.by = 'AIW60.pred', label = TRUE)
 
## check the proportion of cell types predicted in each cluster
t.lables <- as.data.frame(table(seu.q$RNA_snn_res.0.4, seu.q$AIW60.pred))
t.lables$Freq <- as.double(t.lables$Freq)

# try bar chart
ggplot(t.lables, aes(y = Freq, x = Var1, fill = Var2)) + geom_bar(position = "stack", stat= "identity")

# see how accurate the predictions are
seu.q$predicted.id <- ifelse(seu.q$prediction.score.max > 0.95, seu.q$predicted.id, "None")

Idents(seu.q) <- 'predicted.id'
seu.q$AIW60.thresh <- Idents(seu.q)
DimPlot(seu.q, group.by = 'AIW60.thresh', label = TRUE)
table(seu.q$AIW60.pred)
table(seu.q$AIW60.thresh)



# most of the cells are predicted as NPCs in many populations



```

```{r}
# save with predictions so far
#saveRDS(seu.q, "/Users/rhalenathomas/Documents/Data/scRNAseq/PhenoID/scRNAseqSorted/objs/Glia2LabledSeu03102022.RDS")

seu.q <- readRDS("/Users/rhalenathomas/Documents/Data/scRNAseq/PhenoID/scRNAseqSorted/objs/Glia2LabledSeu03102022.RDS")


```


See how many cells are predicted as astrocytes with the threshold

```{r}

DAsubtypes <- readRDS("/Users/rhalenathomas/Documents/Data/scRNAseq/Macosko_Data/DAsubgroups_processed.Rds")

Idents(astro.ref) <- "Cell_Subtype"
DefaultAssay(astro.ref) <- "RNA"

# find the reference anchors
print("finding reference anchors")
anchors <- FindTransferAnchors(reference = DAsubtypes ,query = seu.q, dims = 1:20)
print("getting predictions")
predictions <- TransferData(anchorset = anchors, refdata = astro.ref$Cell_Subtype, k.weight = 10)
seu.q <- AddMetaData(seu.q, metadata = predictions)
print(table(seu.q$predicted.id))

Idents(seu.q) <- 'predicted.id'
# add new dataslot for MBO predicted ID to make the next prediction
seu.q$astro.pred <- Idents(seu.q)
DimPlot(seu.q, group.by = 'astro.pred', label = TRUE)
table(seu.q$astro.pred)

seu.q$predicted.id <- ifelse(seu.q$prediction.score.max > 0.95, seu.q$predicted.id, "none")
print(table(seu.q$predicted.id))

Idents(seu.q) <- 'predicted.id'
seu.q$astro.pred.thresh <- Idents(seu.q)
DimPlot(seu.q, group.by = 'astro.pred.thresh', label = TRUE)
table(seu.q$astro.pred.thresh)


t.lables <- as.data.frame(table(seu.q$RNA_snn_res.0.2, seu.q$astro.pred.thresh))
t.lables$Freq <- as.double(t.lables$Freq)
ggplot(t.lables, aes(y = Freq, x = Var1, fill = Var2)) + geom_bar(position = "stack", stat= "identity") + RotatedAxis() 
top.pred.astro <- as.data.frame(t.lables  %>% group_by(Var1)  %>% top_n(2, Freq))
df.top.astro <- top.pred.astro[order(top.pred.astro$Var1,-top.pred.astro$Freq),]
row.names(df.top.astro) <- NULL


t.lables <- as.data.frame(table(seu.q$RNA_snn_res.0.2, seu.q$astro.pred))
t.lables$Freq <- as.double(t.lables$Freq)
ggplot(t.lables, aes(y = Freq, x = Var1, fill = Var2)) + geom_bar(position = "stack", stat= "identity") + RotatedAxis() 
top.pred.astro <- as.data.frame(t.lables  %>% group_by(Var1)  %>% top_n(2, Freq))
df.top.astro <- top.pred.astro[order(top.pred.astro$Var1,-top.pred.astro$Freq),]
row.names(df.top.astro) <- NULL

# a lot of these cells are also getting labelled as astrocytes


```

Do these get labelled as DA neurons too???

```{r}
seu.q <- readRDS("/Users/rhalenathomas/Documents/Data/scRNAseq/PhenoID/scRNAseqSorted/objs/Glia2LabledSeu03102022.RDS")

```



```{r}

DAsubtypes <- readRDS("/Users/rhalenathomas/Documents/Data/scRNAseq/Macosko_Data/DAsubgroups_processed.Rds")
Idents(DAsubtypes) <- "Cell_Subtype"
da.ref <- subset(DAsubtypes, downsample = 500)

# find the reference anchors
print("finding reference anchors")
anchors <- FindTransferAnchors(reference = da.ref, query = seu.q, dims = 1:20)
print("getting predictions")
predictions <- TransferData(anchorset = anchors, refdata = da.ref$Cell_Subtype, k.weight = 10)
seu.q <- AddMetaData(seu.q, metadata = predictions)
print(table(seu.q$predicted.id))

Idents(seu.q) <- 'predicted.id'
# add new dataslot for MBO predicted ID to make the next prediction
seu.q$da.pred <- Idents(seu.q)
DimPlot(seu.q, group.by = 'da.pred', label = TRUE)
table(seu.q$da.pred)

seu.q$predicted.id <- ifelse(seu.q$prediction.score.max > 0.95, seu.q$predicted.id, "none")

Idents(seu.q) <- 'predicted.id'
seu.q$da.pred.thresh <- Idents(seu.q)
DimPlot(seu.q, group.by = 'da.pred.thresh', label = TRUE)
table(seu.q$da.pred.thresh)


t.lables <- as.data.frame(table(seu.q$RNA_snn_res.0.2, seu.q$da.pred.thresh))
t.lables$Freq <- as.double(t.lables$Freq)
ggplot(t.lables, aes(y = Freq, x = Var1, fill = Var2)) + geom_bar(position = "stack", stat= "identity") + RotatedAxis() 
top.pred.astro <- as.data.frame(t.lables  %>% group_by(Var1)  %>% top_n(2, Freq))
df.top.astro <- top.pred.astro[order(top.pred.astro$Var1,-top.pred.da$Freq),]
row.names(df.top.astro) <- NULL


t.lables <- as.data.frame(table(seu.q$RNA_snn_res.0.2, seu.q$astro.pred))
t.lables$Freq <- as.double(t.lables$Freq)
ggplot(t.lables, aes(y = Freq, x = Var1, fill = Var2)) + geom_bar(position = "stack", stat= "identity") + RotatedAxis() 
top.pred.astro <- as.data.frame(t.lables  %>% group_by(Var1)  %>% top_n(2, Freq))
df.top.astro <- top.pred.astro[order(top.pred.astro$Var1,-top.pred.astro$Freq),]
row.names(df.top.astro) <- NULL


# after thresholding very few cells are predicted as neurons



```

Compare predictions - make a predictions table

```{r}

# AIW002 120 days predictions - take the thresholded options
t.lables <- as.data.frame(table(seu.q$RNA_snn_res.0.2, seu.q$AIW120.thresh))
t.lables$Freq <- as.double(t.lables$Freq)
ggplot(t.lables, aes(y = Freq, x = Var1, fill = Var2)) + geom_bar(position = "stack", stat= "identity") + RotatedAxis() 
top.pred.celltype.AIW120 <- as.data.frame(t.lables  %>% group_by(Var1)  %>% top_n(2, Freq))
df.top.aiw120 <- top.pred.celltype.AIW120[order(top.pred.celltype.AIW120$Var1,-top.pred.celltype.AIW120$Freq),]
row.names(df.top.aiw120) <- NULL
df.top.aiw120$I <- row.names(df.top.aiw120)

# AIW002 60 days predictions
t.lables <- as.data.frame(table(seu.q$RNA_snn_res.0.2, seu.q$AIW60.thresh))
t.lables$Freq <- as.double(t.lables$Freq)
ggplot(t.lables, aes(y = Freq, x = Var1, fill = Var2)) + geom_bar(position = "stack", stat= "identity") + RotatedAxis() 
top.pred.celltype.AIW60 <-as.data.frame(t.lables  %>% group_by(Var1)  %>% top_n(2, Freq))
df.top.aiw60 <- top.pred.celltype.AIW60[order(top.pred.celltype.AIW60$Var1,-top.pred.celltype.AIW60$Freq),]
row.names(df.top.aiw60) <- NULL
df.top.aiw60$I <- row.names(df.top.aiw60)


# AST23 165 days predictions
t.lables <- as.data.frame(table(seu.q$RNA_snn_res.0.2, seu.q$MBOAST23.thresh))
t.lables$Freq <- as.double(t.lables$Freq)
ggplot(t.lables, aes(y = Freq, x = Var1, fill = Var2)) + geom_bar(position = "stack", stat= "identity") + RotatedAxis() 
top.pred.celltype.AST23 <- as.data.frame(t.lables  %>% group_by(Var1)  %>% top_n(2, Freq))
df.top.AST23 <- top.pred.celltype.AST23[order(top.pred.celltype.AST23$Var1,-top.pred.celltype.AST23$Freq),]
row.names(df.top.AST23) <- NULL
df.top.AST23$I <- row.names(df.top.AST23)

# add the threshold Astro predictions 
t.lables <- as.data.frame(table(seu.q$RNA_snn_res.0.2, seu.q$astro.pred.thresh))
t.lables$Freq <- as.double(t.lables$Freq)
ggplot(t.lables, aes(y = Freq, x = Var1, fill = Var2)) + geom_bar(position = "stack", stat= "identity") + RotatedAxis() 
top.pred.celltype.astro <- as.data.frame(t.lables  %>% group_by(Var1)  %>% top_n(2, Freq))
df.top.astro <- top.pred.celltype.astro[order(top.pred.celltype.astro$Var1,-top.pred.celltype.astro$Freq),]
row.names(df.top.astro) <- NULL
df.top.astro$I <- row.names(df.top.astro)

# add the neurons predictions 
t.lables <- as.data.frame(table(seu.q$RNA_snn_res.0.2, seu.q$da.pred.thresh))
t.lables$Freq <- as.double(t.lables$Freq)
ggplot(t.lables, aes(y = Freq, x = Var1, fill = Var2)) + geom_bar(position = "stack", stat= "identity") + RotatedAxis() 
top.pred.celltype.da <- as.data.frame(t.lables  %>% group_by(Var1)  %>% top_n(2, Freq))
df.top.da <- top.pred.celltype.da[order(top.pred.celltype.da$Var1,-top.pred.celltype.da$Freq),]
row.names(df.top.da) <- NULL
df.top.da$I <- row.names(df.top.da)

pred.table <- merge(df.top.AST23, df.top.aiw60, by = 'I', all = TRUE)
pred.table <- merge(pred.table, df.top.aiw120, by = 'I')
pred.table <- merge(pred.table, df.top.astro, by = 'I')
pred.table <- merge(pred.table, df.top.da, by = 'I')

pred.table


```

Predicted cluster annotations
0	Unknown/ NPC
1	RG
2	astro
3	RG
4	neurons
5	RG

Look at gene lists with known markers

```{r}

Idents(seu.q) <- 'RNA_snn_res.0.2'

# many cell types list
feature_list = c("MKI67","SOX2","POU5F1","DLX2","PAX6","SOX9","HES1","NES","RBFOX3","MAP2","NCAM1","CD24","GRIA2","GRIN2B","GABBR1","GAD1","GAD2","GABRA1","GABRB2","TH","ALDH1A1","LMX1B","NR4A2","CORIN","CALB1","KCNJ6","CXCR4","ITGA6","SLC1A3","CD44","AQP4","S100B", "PDGFRA","OLIG2","MBP","CLDN11","VIM","VCAM1")

DoHeatmap(seu.q, features = feature_list, size=3, angle =90, group.bar.height = 0.02)
DotPlot(seu.q, features = feature_list) +RotatedAxis()

# Dopaminergic markers
PD_poulin = c("TH","SLC6A3","SLC18A2","SOX6","NDNF","SNCG","ALDH1A1","CALB1","TACR2","SLC17A6","SLC32A1","OTX2","GRP","LPL","CCK","VIP")

DoHeatmap(seu.q, features = PD_poulin, size=3, angle =90, group.bar.height = 0.02)
DotPlot(seu.q, features = PD_poulin)+RotatedAxis()

ealryNeur = c("DCX","NEUROD1","TBR1")
proliferation = c("PCNA","MKI67")
neuralstem = c("SOX2","NES","PAX6","MASH1")

feature_list <- c("DCX","NEUROD1","TBR1","PCNA","MKI67","SOX2","NES","PAX6","MASH1")
DoHeatmap(seu.q, features = feature_list, size=3, angle =90, group.bar.height = 0.02)
DotPlot(seu.q, features = feature_list)+RotatedAxis()


mat_neuron = c("RBFOX3","SYP","DLG45","VAMP1","VAMP2","TUBB3","SYT1","BSN","HOMER1","SLC17A6") 
# NeuN is FOX3 - RBFOX3
# PSD95 also SP-90 or DLG4
# VGLUT2 is SLC17A6
DoHeatmap(seu.q, features = mat_neuron, size=3, angle =90, group.bar.height = 0.02)
# cluster 4 also show mature neuron markers
DotPlot(seu.q, features = mat_neuron)+RotatedAxis()
# excitatory neuron markers
ex = c("GRIA2","GRIA1","GRIA4","GRIN1","GRIN2B","GRIN2A","GRIN3A","GRIN3","GRIP1","CAMK2A")
DoHeatmap(seu.q, features = ex, size=3, angle =90, group.bar.height = 0.02)
DotPlot(seu.q, features = ex)+RotatedAxis()
# inhibitory neuron markers
inh = c("GAD1","GAD2", "GAT1","PVALB","GABR2","GABR1","GBRR1","GABRB2","GABRB1","GABRB3","GABRA6","GABRA1","GABRA4","TRAK2")
DoHeatmap(seu.q, features = inh, size=3, angle =90, group.bar.height = 0.02)
DotPlot(seu.q, features = inh)+RotatedAxis()
# cluster 4 is more excitatory than inhbitory but neither marker set has much expression 

### glia markers
microglia = c("PTPRC","AIF1","ADGRE1")  # ADGRE1 is a microglia marker F4/80, CD45 is PTPRC, gene name IBA1 is AIF1
astolgNPCpromicro = c("GFAP","S100B","SLC1A2","MBP","SOX10","SPP1","DCX","NEUROD1","TBR1","PCNA","MKI67","PTPRC","AIF1","ADGRE1")
# note GLT1 is EAAT2 which is SLC1A2 glutatmate transporter
# epithelial
epi = c("HES1","HES5","SOX2","SOX10","NES","CDH1","NOTCH1") # e-cadherin is CDH1

DoHeatmap(seu.q, features = astolgNPCpromicro, size=3, angle =90, group.bar.height = 0.02)
DotPlot(seu.q, features = astolgNPCpromicro)+RotatedAxis()
# cluster 4 is more excitatory than inhbitory but neither marker set has much expression 
DoHeatmap(seu.q, features = epi, size=3, angle =90, group.bar.height = 0.02)
DotPlot(seu.q, features = epi)+RotatedAxis()

# also add Radial glia marker overlap with Glia and Neurons

features <- c("PTPRC","AIF1","ADGRE1", "VIM", "TNC","PTPRZ1","FAM107A","HOPX","LIFR",
              "ITGB5","IL6ST")
DoHeatmap(seu.q, features = features, size=3, angle =90, group.bar.height = 0.02)
DotPlot(seu.q, features = features)+RotatedAxis()

# radial glia markers
rg <- c("VIM","NES","PAX6","HES1","EAAT1","NCAD1","SOX2","FABP7")
DoHeatmap(seu.q, features = rg, size=3, angle =90, group.bar.height = 0.02)
DotPlot(seu.q, features = rg)+RotatedAxis()

# NPC and radial glia are very similar

```
Marker expression predictions
Cluster 0 - unknown
Cluster 1 - RG
Cluster 2 - unknown
Cluster 3 - RG
cluster 4 - immature neurons
Cluster 5 - RG, opc


Check the levels of RNA in each cluster 

```{r}
VlnPlot(seu.q, features = "nFeature_RNA")

```
Cluster 0 and 2 have fewer sequences than other groups and thus no markers
Possibly remove these is they don't come up with some markers

Find cluster markers

```{r}
Idents(seu.q) <- 'RNA_snn_res.0.2'
ClusterMarkers <- FindAllMarkers(seu.q, only.pos = TRUE)

top5 <- ClusterMarkers %>% group_by(cluster) %>% top_n(n=5, wt = avg_log2FC)
DoHeatmap(seu.q, features = top5$gene, size=3, angle =90, group.bar.height = 0.02)

#write.csv(ClusterMarkers,"/Users/rhalenathomas/Documents/Data/scRNAseq/PhenoID/scRNAseqSorted/Glia2RGClusterMarkers_new.csv")

Idents(seu.q) <- 'RNA_snn_res.0.1'
ClusterMarkers <- FindAllMarkers(seu.q, only.pos = TRUE)

top5 <- ClusterMarkers %>% group_by(cluster) %>% top_n(n=5, wt = avg_log2FC)
DoHeatmap(seu.q, features = top5$gene, size=3, angle =90, group.bar.height = 0.02)


```
Markers of 2 are matching with 5 possibly merge these together
Cluster 0 markers don't look up regulated but the list is long

Look at the libraries

```{r}
library(enrichR)

setEnrichrSite("Enrichr") # Human genes
# list of all the databases

# libaries with cell types

db <- c('Descartes_Cell_Types_and_Tissue_2021',
        'CellMarker_Augmented_2021','Azimuth_Cell_Types_2021')

# enrichr(genes, databases = NULL)

#I'll run the clusters one at a time

N1.c0 <- ClusterMarkers %>% filter(cluster == 0 & avg_log2FC > 0)
genes <- N1.c0$gene

N1.c0.Er <- enrichr(genes, databases = db)
plotEnrich(N1.c0.Er[[1]], showTerms = 20, numChar = 40, y = "Count", orderBy = "P.value")
plotEnrich(N1.c0.Er[[2]], showTerms = 20, numChar = 40, y = "Count", orderBy = "P.value")
plotEnrich(N1.c0.Er[[3]], showTerms = 20, numChar = 40, y = "Count", orderBy = "P.value")


N1.Er.genes.1 <- N1.c0.Er[[1]] %>% select(Term, Genes, Combined.Score)
N1.Er.genes.1

N1.Er.genes.2 <- N1.c0.Er[[2]] %>% select(Term, Genes, Combined.Score)
N1.Er.genes.2

N1.Er.genes.3 <- N1.c0.Er[[3]] %>% select(Term, Genes, Combined.Score)
N1.Er.genes.3






```

Add some cell type annotations

```{r}

Idents(seu.q) <- 'RNA_snn_res.0.2'

cluster.ids <- c("Glia1","RG1","Glia2","RG2","NeuronsImmature","RG3")

names(cluster.ids) <- levels(seu.q)
seu.q <- RenameIdents(seu.q, cluster.ids)
seu.q$subgroups <- Idents(seu.q)

#DimPlot(seu.q, group.by = 'RNA_snn_res.0.2', label = TRUE)
DimPlot(seu.q, reduction = "umap", label = TRUE, group.by = 'subgroups', repel = TRUE)



```

```{r}
# save file
saveRDS(seu.q, "/Users/rhalenathomas/Documents/Data/scRNAseq/PhenoID/scRNAseqSorted/objs/Glia2LabledSeu03102022.RDS")

```

Main cell groups

```{r}

Idents(seu.q) <- 'RNA_snn_res.0.2'

cluster.ids <- c("RG","RG","RG","RG","NeuronsImmature","RG")

names(cluster.ids) <- levels(seu.q)
seu.q <- RenameIdents(seu.q, cluster.ids)
seu.q$Cell_Types <- Idents(seu.q)

#DimPlot(seu.q, group.by = 'RNA_snn_res.0.2', label = TRUE)
DimPlot(seu.q, reduction = "umap", label = TRUE, group.by = 'Cell_Types', repel = TRUE)

saveRDS(seu.q, "/Users/rhalenathomas/Documents/Data/scRNAseq/PhenoID/scRNAseqSorted/objs/Glia2LabledSeu03102022.RDS")

```

Proportions of cell types

```{r}

table(seu.q$Cell_Types)
dim(seu.q)

prp <- as.data.frame(table(seu.q$Cell_Types))
prp

prp$prop <- prp$Freq/sum(prp$Freq)*100
prp$Sample <- 'RadialGlia'
prp


```

I'll calculate the proportions for each cell type and make a table or plot in the comparison workbook. 



